The ability of artificial intelligence (AI) to engage in reciprocal communication introduces new social dynamics in the marketing domain. The study explores this social dimension of consumer-AI interaction through key variables such as alienation, anthropomorphisation, interaction context, and identity-related motivations. Using a 2 × 2 experimental design (alienating vs. non-alienating interaction; website vs. social media) with 245 participants, several findings emerged: (1) a favourable predisposition toward AI reduces alienation and facilitates the attribution of human-like traits; (2) prior experience with AI encourages anthropomorphisation but does not mitigate perceived disconnection in the interaction; (3) alienation intensifies the tendency to objectify AI, regardless of the interaction context; (4) merely engaging in a particular digital context does not directly alter alienation or anthropomorphisation; however, a positive attitude toward the environment in which the interaction occurs reduces disconnection and discourages AI objectification; and (5) individuals with high motivation for uniqueness experience more pronounced alienation but, as a compensatory response, tend to humanise AI to a greater extent. This research contributes to the understanding of the relational dynamics between consumers and AI, emphasising the importance of balancing functional efficiency with relational quality. Practical applications for professionals seeking to implement AI-driven solutions with a consumer-centric approach are proposed.

In the last few decades, graph domination problems have attracted the attention of both academics and practitioners. In these problems, a subset of vertices is selected such that every vertex in the graph is either in the subset or adjacent to at least one selected vertex. One of the most extended variants is the Roman Domination Problem (RDP), where vertices are assigned values to ensure coverage under specific protection rules. This research addresses the Double Roman Domination Problem (DROMDP), a more restrictive extension of RDP in which stronger domination conditions are imposed to guarantee coverage even under potential vertex failures. In this paper, an algorithm based on the Iterated Local Search (ILS) framework is proposed, considering the use of two constructive procedures, two local search methods, and two perturbation mechanisms to find high-quality solutions. The results obtained are compared with the state-of-the-art method, based on Ant Colony Optimization, with ILS emerging as the most competitive algorithm for DROMDP. These results are supported by an extensive computational experimentation, including an ablation study of the different components, statistical tests, and a Bayesian analysis on the probability of ILS for being the best algorithm for any instance.

The comparison of dental records is a standardized technique in forensic odontology used to speed up the identification of individuals in multiple-comparison scenarios. Specifically, the odontogram comparison is a procedure to compute criteria that will be used to perform a ranking. State-of-the-art automatic methods either make use of simple techniques, without utilizing the full potential of the information obtained from a comparison, or their internal behavior is not known due to the lack of detailed description or peer-reviewed publications. This work aims to design aggregation mechanisms to automatically compare pairs of dental records that can be understood and validated by experts, improving the current methods. To do so, we introduce different aggregation approaches using the state-of-the-art codification, based on seven different criteria. In particular, we study the performance of: (i) data-driven lexicographical order-based aggregations, (ii) well-known fuzzy logic aggregation methods, and (iii) machine learning techniques as aggregation mechanisms. To validate our proposals, 215 real-world forensic cases from two different populations have been used. The results obtained show how the use of white-box machine learning techniques as aggregation models (average ranking from 2.02 to 2.21) are able to improve the state of the art (average ranking of 3.91) without compromising the explainability and interpretability of the method.

Opinion Dynamics (OD) models are a class of agent-based models that describe how opinions evolve within a population. In these models, individual opinions change through interactions governed by an opinion fusion rule that specifies how updates occur. Despite their simplicity, OD models offer interpretable mechanisms for understanding the collective dynamics of opinion formation. However, most existing approaches focus on the emergence of consensus, fragmentation, or polarization, while overlooking real-world scenarios characterized by highly oscillatory trends. This study addresses this limitation by evaluating the ability of several OD models extended with dynamic parameters to reproduce oscillatory dynamics. To this end, we formulate an optimization problem solved via evolutionary algorithms. The methodology is first validated on synthetic target series to assess the intrinsic oscillatory capabilities of the models, and subsequently applied to a real-world dataset of public opinion about immigration, drawn from the monthly barometer of the Spanish Sociological Research Center. Results show that the Agent-independent Time-based Bounded Confidence and Repulsion (ATBCR) model, which combines confidence-based and polarization-based update mechanisms, achieves the best performance. The optimized model closely reproduces historical opinion fluctuations while exhibiting interpretable, human-like patterns of collective evolution.

ABSTRACT Age‐at‐death estimation is an arduous task in human identification based on characteristics such as appearance, morphology or ossification patterns in skeletal remains. This process is performed manually, although in recent years there have been several studies that attempt to automate it. One of the most recent approaches involves considering interpretable machine learning methods, obtaining simple and easily understandable models. The ultimate goal is not to fully automate the task but to obtain an accurate model supporting the forensic anthropologists in the age‐at‐death estimation process. We propose a semi‐automatic method for age‐at‐death estimation based on nine pubic symphysis traits identified from Todd's pioneering method. Genetic programming is used to learn simple mathematical expressions following a symbolic regression process, also developing feature selection. Our method follows a component‐scoring approach where the values of the different traits are evaluated by the expert and aggregated by the corresponding mathematical expression to directly estimate the numeric age‐at‐death value. Oversampling methods are considered to deal with the strongly imbalanced nature of the problem. State‐of‐the‐art performance is achieved thanks to an interpretable model structure that allows us to both validate existing knowledge and extract some new insights in the discipline.

Artificial intelligence (AI) systems are increasingly adopted in high‐stakes domains such as healthcare and finance, so the demand for transparency and interpretability has grown substantially. EXplainable AI (XAI) methods have emerged to address this challenge, but individual techniques often offer limited, fragmented insights. This paper introduces Meta‐explainers, a novel ensemble‐based XAI framework that integrates multiple explanation types—specifically relevance‐based and counterfactual methods—into unified, multifaceted and complementary meta‐explanations. Inspired by meta‐classification principles, our approach structures the explanation process into five stages: generation, grouping, evaluation, aggregation, and visualization. Each stage is designed to preserve the unique strengths of individual XAI techniques while enhancing their interpretability and coherence when combined. Experimental results on both image (MNIST) and tabular (Breast Cancer) datasets show that Meta‐explainers consistently outperform individual and state‐of‐the‐art ensemble explanation methods in terms of explanation quality, as measured by established metrics. This work paves the way toward more holistic and user‐centered AI explainability with a flexible methodology that can be extended to incorporate additional explanation paradigms.

Explainability has become one of the leading research topics within Artificial Intelligence (AI) in the last few years, as it has increased the confidence and credibility of “black box” models, such as deep neural networks . However, the evaluation of the explanations provided by different explainability approaches remains a hot research topic. This evaluation enables the possibility of comparing different existing techniques and would play a crucial role in improving the auditability of AI-based systems. The current literature on the subject considers that the evaluation of explainability methods can be approached in two ways: qualitative and quantitative . While qualitative evaluations are based on assumptions induced by human understanding that are hard to develop and prone to introduce certain cognitive biases, quantitative ones avoid these biases by excluding the human expert from the evaluation process. However, the main challenge in quantitatively evaluating an explanation is the lack of ground truth specifying what defines a correct explanation. In this paper, we propose an evaluation measure that quantifies the Level of Strength of an Explanation (LSE), i.e., the extent to which the explanation produced by a post-hoc explainability method supports the class predicted by a classifier. Our proposal is inspired by the semantics underlying the Likelihood Ratio in evaluating forensic evidence, which is defined as the weight to be attributed to a piece of forensic evidence according to the prosecution and defense propositions. To validate our proposal, nine popular explainability techniques are compared across two deep neural architectures dedicated to image classification and three classifiers for binary classification over tabular datasets. In addition, we use MetaQuantus as a meta-evaluation approach. Results from our experimental study reveal that GradCAM and LRP outperform the other explainability methods in terms of the proposed LSE measure.

Opinion Dynamics models in social networks are a valuable tool to study how opinions evolve within a population. However, these models often rely on agent-level parameters that are difficult to measure in a real population. This is the case of the confidence threshold in opinion dynamics models based on bounded confidence, where agents are only influenced by other agents having a similar opinion (given by this confidence threshold). Consequently, a common practice is to apply a universal threshold to the entire population and calibrate its value to match observed real-world data, despite being an unrealistic assumption. In this work, we propose an alternative approach using graph neural networks to infer agent-level confidence thresholds in the opinion dynamics of the Hegselmann-Krause model of bounded confidence. This eliminates the need for additional simulations when faced with new case studies. To this end, we construct a comprehensive synthetic training dataset that includes different network topologies and configurations of thresholds and opinions. Through multiple training runs utilizing different architectures, we identify GraphSAGE as the most effective solution, achieving a coefficient of determination$R^{2}$above 0.7 in test datasets derived from real-world topologies. Remarkably, this performance holds even when the test topologies differ in size from those considered during training.

Agent-based models (ABM) stand as a well-established paradigm in developing computational models. ABM enable the simulation of complex systems by consolidating individual-level interactions through an underlying artificial social network. Upon the accurate construction of a model, experts can employ it as a decision support system for assessing policies in hypothetical what-if scenarios and gaining insights into the operational dynamics of the target system. However, ABM still face diverse challenges such as learning realistic agents' behavioral patterns to model real-world conducts and habits. Existing research shows that machine learning techniques, when used in ABM, can address such challenges. This work focuses on using evolutionary algorithms (EAs) to learn agents' behavior through the definition of their micro-rules, aiming to accurately model them, and thus improving the global performance of the system. To do so, we model the machine learning problem and undertake a comparative analysis of seven EAs, encompassing both classical and recent approaches, to learn agents' micro-rules with a specific focus on consumers' behavior for brand selection. Our investigation involves the assessment of the performance of each EA across problem instances derived from an ABM applied to real-world marketing domains such as dairy products and automakers. The findings of our study reveal a distinct dominance of SHADE-ILS and DECC-g over the remaining algorithms considered. Furthermore, we underline the advantages of these methods to assist modelers in selecting among the best solutions.

The present study addresses the problem of semiautomatic age-at-death estimation from pubic symphysis, a crucial yet complex task in forensic anthropology. Its accuracy directly depends on the quality of the pubic bone trait labeling developed by the forensic practitioners, affected by an inherent uncertainty in their definition. As interpretability is a mandatory requirement, we propose an approach where the model design is based on evolutionary learning, considering genetic programming to frame the problem as a symbolic regression task. Additionally, ensemble learning is considered to address the challenges posed by noise, uncertainty, and conflicting annotations inherent in data collected from multiple subjects. Ensemble learning provides an effective approach to navigate these challenges by facilitating consensus-building through decision making and information fusion. Hence, observer committees are formed, comprising multiple forensic specialists with different skills and expertise which provide alternative annotations. Several ensemble configurations combining different weak learners and aggregation operators are tested to assess their effectiveness in improving accuracy and reliability in age-at-death predictions. Their performance is compared against models trained on single annotations, revealing an improvement in predictive accuracy. The obtained results also highlight the benefits of incorporating diverse perspectives to address the complexities associated with human variability and anatomical assessments.

One of the most challenging questions in the film industry is to rank superstars, which ultimately affects some performance indicators like movie success. In this work, we address this question by means of opinion dynamics models, where the evolution of opinions in a population is analyzed. We apply a model of this kind to study the evolution of opinions about a set of well-known movie superstars in a real-world population. Also, we use real-world data from a specialized cinema website to model mass communication processes (representing film releases and their related news and marketing campaigns), and to measure the performance of our model. Our results show that the proposed model is able to accurately represent this complex system, where the opinion dynamics of superstars are mostly driven by emotional mechanisms, and reveal that film releases and their corresponding marketing campaigns only have a short term effect on those opinions. To the best of our knowledge, this is the first work that applies opinion dynamics models to the study of opinions about superstars in the film industry.

Although deep learning models can solve complex prediction problems, they have been criticized for being ‘black boxes’. This implies that their decisions are difficult, if not impossible, to explain by simply inspecting their internal knowledge structures. Explainable Artificial Intelligence has attempted to open the black-box through model-specific and agnostic post-hoc methods that generate visualizations or derive associations between the problem features and the model predictions. This paper proposes a new method, termed REPROT, that explains the decisions of complex deep learning architectures based on local reducts of an image. A ‘reduct’ is a set of sufficiently descriptive features that can fully characterize the acquired knowledge. The created reducts are used to build a ‘prototype image’ that visually explains the inference obtained by a black-box model for an image. We focus on deep learning architectures whose complexity and internal particularities demand adapting existing model-specific explanation methods, making the explanation process more difficult. Experimental results show that the black-box model can detect an object using the prototype image generated from the reduct. Hence, the explanations will be given by “the minimum set of features sufficient for the neural model to detect an object”. The confidence scores obtained by architectures such as Inception, Yolo, and Mask R-CNN are higher for prototype images built from the reduct than those built from the most important superpixels according to the LIME method. Moreover, the target object is not detected on several occasions through the LIME output, thus supporting the superiority of the proposed explanation method.

Social network analysis is a popular tool to understand the relationships between interacting agents by studying the structural properties of their connections. However, this kind of analysis can miss some of the domain-specific knowledge available in the original information domain and its propagation through the associated network. In this work, we develop an extension of classical social network analysis to incorporate external information from the original source of the network. With this extension we propose a new centrality measure, the semantic value, and a new affinity function, the semantic affinity, that establishes fuzzy-like relationships between the different actors in the network. We also propose a new heuristic algorithm based on the shortest capacity problem to compute this new function. As an illustrative case study, we use the novel proposals to analyze and compare the gods and heroes from three different classical mythologies: 1) Greek; 2) Celtic; and 3) Nordic. We study the relationships of each individual mythology and those of the common structure that is formed when we fuse the three of them. We also compare our results with those obtained using other existing centrality measures and embedding approaches. In addition, we test the proposed measures on a classical social network, the Reuters terror news network, as well as in a Twitter network related to the COVID-19 pandemic. We found that the novel method obtains more meaningful comparisons and results than previous existing approaches in every case.

Automatic art analysis employs different image processing techniques to classify and categorize works of art. When working with artistic images, we need to take into account further considerations compared to classical image processing. This is because artistic paintings change drastically depending on the author, the scene depicted, and their artistic style. This can result in features that perform very well in a given task but do not grasp the whole of the visual and symbolic information contained in a painting. In this article, we show how the features obtained from different tasks in artistic image classification are suitable to solve other ones of similar nature. We present different methods to improve the generalization capabilities and performance of artistic classification systems. Furthermore, we propose an explainable artificial intelligence method to map known visual traits of an image with the features used by the deep learning model considering fuzzy rules. These rules show the patterns and variables that are relevant to solve each task and how effective is each of the patterns found. Our results show that compared to multitask learning, our proposed context-aware features can achieve up to 19% more accurate results when using the residual network architecture and 3% when using ConvNeXt. We also show that some of the features used by these models can be more clearly correlated to visual traits in the original image than other kinds of features.

Cephalometric landmarks are used in many forensic tasks of great relevance. Nevertheless, the automatic localization of such points is greatly underdeveloped in the scientific literature, especially on in-the-wild images where no published work is available. Inspired by state-of-the-art automatic facial landmark localization research, we present a method based on a cascade of conditional convolutional networks for predicting high-resolution cephalometric landmarks under specific conditions: using a size-limited dataset of in-the-wild images usually handled by forensic anthropologists. Every contribution is thoroughly ablated and validated. We compare our proposal against top-performing standard facial landmark localization methods. Furthermore, we conduct a user study comparing our performance against expert annotators on a different problem-specific dataset. The results show that we outperform competing methods in a cephalometric landmarks dataset by a large margin, two times better than the closest one, and achieve human-like performance in half of the cases. For its good results, our proposal will be included in Skeleton-ID, a commercial solution for forensic identification assisted by artificial intelligence.

Evolutionary Computer Vision (ECV) is at the intersection of two major research fields of artificial intelligence: 1) computer vision (CV) and 2) evolutionary computation (EC). This special issue brings an overview of state-of-the-art contributions to the latest research and development in the discipline. CV includes methods for acquiring, processing, analyzing, and understanding images. The aim is to design computational models of human and animal perception. ECV is an interdisciplinary research area where analytical methods combined with powerful stochastic optimization and metaheuristic approaches produced human-competitive results. From an engineering standpoint, ECV aims to design software and hardware solutions useful for solving challenging CV problems. From a scientific viewpoint, the goal is to enhance our current understanding of visual processing in nature and replicate this within a seeing machine. ECV is a well-established research discipline as evolutionary algorithms are more efficient than classical optimization approaches for the discontinuous, nondifferentiable, multimodal, and noisy search, optimization, and learning problems arising in many CV tasks. EC has also demonstrated its ability as a robust approach to cope with the fundamental steps of image processing, image analysis, and image understanding included in the CV pipeline (e.g., restoration, segmentation, registration, classification, reconstruction, or tracking).

The spreading and evolution of opinions is the key question studied in opinion dynamics. This is especially relevant in applications that depend on (possibly evolving) opinions, such as decision-making (the process of selecting an alternative from a set of possible options). In this work, we extend an existing communication framework of opinion dynamics in order to study the effects of mass communication, such as propaganda or advertisement campaigns. In this framework, fuzzy linguistic 2-tuples are used to represent opinions, which is a realistic representation of this qualitative information, and the communication is divided into three independent sub-processes, which represent the propagation of opinions in a more realistic manner, including a social network to represent agents' interactions and an awareness deactivation mechanism to model the awareness dynamics in the system (i.e., options for which agents have opinions). However, other sources of massive information can also influence opinions. To model them, in this work we present a mass communication mechanism, and integrate it in the previous communication framework. The resulting opinion dynamics model can be useful to analyze real-world scenarios where opinions evolve as a consequence of interactions between agents and also due to mass communication campaigns. In fact, our experimental results show that mass communication can have a major impact on the opinion evolution of the population.

Age estimation is a fundamental task in forensic anthropology for both the living and the dead. The procedure consists of analyzing properties such as appearance, ossification patterns, and morphology in different skeletonized remains. The pubic symphysis is extensively used to assess adults’ age-at-death due to its reliability. Nevertheless, most methods currently used for skeleton-based age estimation are carried out manually, even though their automation has the potential to lead to a considerable improvement in terms of economic resources, effectiveness, and execution time. In particular, explainable machine learning emerges as a promising means of addressing this challenge by engaging forensic experts to refine and audit the extracted knowledge and discover unknown patterns hidden in the complex and uncertain available data. In this contribution we address the automation of the decision making process of Todd’s pioneering age assessment method to assist the forensic practitioner in its application. To do so, we make use of the pubic bone data base available at the Physical Anthropology lab of the University of Granada. The machine learning task is significantly complex as it becomes an imbalanced ordinal classification problem with a small sample size and a high dimension. We tackle it with the combination of an ordinal classification method and oversampling techniques through an extensive experimental setup. Two forensic anthropologists refine and validate the derived rule base according to their own expertise and the knowledge available in the area. The resulting automatic system, finally composed of 34 interpretable rules, outperforms the state-of-the-art accuracy. In addition, and more importantly, it allows the forensic experts to uncover novel and interesting insights about how Todd’s method works, in particular, and the guidelines to estimate age-at-death from pubic symphysis characteristics, generally.

In the bounded confidence framework, agents’ opinions evolve as a result of interactions with other agents having similar opinions. Thus, consensus or fragmentation of opinions can be reached, but not extremization (the evolution of opinions towards an extreme value). In contrast, when repulsion mechanisms are at work, agents with distant opinions interact and repel each other, leading to extremization. This work proposes a general opinion dynamics framework of bounded confidence and repulsion, which includes social network interactions and agent-independent time-varying rationality. We extensively analyze the performance of our model to show that the degree of extremization among a population can be controlled by the repulsion rule, and social networks promote extreme opinions. Agent-based rationality and time-varying adaptation also bear a strong impact on opinion dynamics. The high accuracy of our model is determined in a real-world social network well referenced in the literature, the Zachary Karate Club (with a known ground truth). Finally, we use our model to analyze the extremization of opinions in a real-world scenario, in Spain: a marketing action for the Netflix series “Narcos”.

Calibration is a crucial task on building valid models before exploiting their results. This process consists of adjusting the model parameters in order to obtain the desired outputs. Automatic calibration can be performed by using an optimization algorithm and a fitness function, which involves a deviation measure to compare the time series coming from the model. In this paper, we apply a memetic IPOP-CMA-ES for the calibration of an agent-based model and we study the effect of different deviation measures in this calibration problem. Classical metrics calculate the mean point-to-point error, but we also propose using an extension of dynamic time warping, which considers trend series evolution. In order to determine if calibrating with an specific metric leads to better solutions, we carry out an exhaustive experimentation by including statistical tests, analysis on the values of the calibrated parameters, and qualitative results. Our results show IPOP-CMA-ES obtains better performance than a genetic algorithm. In addition, MAE, MAPE and Soft-DTW are the metrics which report best results, although we get a similar behavior for all of them.

Comparative radiography is the forensic anthropology technique in which ante-mortem (AM) and post-mortem (PM) radiographic materials (e.g., X-ray images or CTs) are compared in order to determine the identity of a deceased human being. One of the most commonly used anatomical structures in comparative radiography are the frontal sinuses. The frontal sinuses are osseous cavities located in the skull, which are used in forensic identification tasks due to their singularity and high identification power. In order to automate the comparison of frontal sinuses in AM and PM materials, it is necessary to perform the registration of these materials (i.e., it is necessary to carry out the alignment of these anatomical regions). However, the manual alignment of these structures is a time-consuming and subjective process. In order to tackle this problem, this paper presents an automatic frontal sinuses registration method in comparative radiography using real-coded evolutionary algorithms (RCEAs). The task is formulated as a 2D-3D image registration problem using a 9 Degrees of Freedom perspective transformation model; two RCEAs (DE and MVMOSH) are compared in the minimization of the registration cost function, and the best of them (MVMO-SH) is applied to an identification scenario including 50X-ray images and 50 CTs. The results obtained show that the proposed automatic identification system is able to filter more than 80% of the sample.

Agent-based modeling (ABM) is a simulation paradigm to model complex systems by defining heterogeneous individual-level behaviors in a bottom-up approach. ABM is typically employed to simulate markets to study consumer decisions and to see how consumers make their purchase decisions. In this work, we present a marketing ABM where consumer perceptions are modeled using 2-tuple fuzzy linguistic variables. These variables represent the opinions the consumers have on the different features of every product, which drive their decisions (e.g., price or quality). In contrast to numerical or crisp values, fuzzy linguistic variables are a realistic representation of these qualitative aspects. In our ABM, agents use a decision-making heuristic to select a product, which is based on those perceptions and a probabilistic utility maximization rule. This process requires a fuzzy aggregation of the perceptions of every product, based on an ordered weighted average (OWA). In addition, consumers can be aware or unaware of each product in the market. In our ABM, we model this information by introducing a brand awareness filter when applying the decision-making heuristic. Thus, consumer agents can only select those products they are aware of. Our experimental results show that our realistic representation of the consumer preferences is more accurate than other existing approaches.

Understanding consumer behaviors and how consumers react to marketing campaigns and viral word-of-mouth processes is crucial for marketers. Classical approaches try to infer this information from a global top-down perspective. However, a more suitable and natural approach is to model consumer behaviors in a heterogeneous and decentralized bottom-up approach. In this case, each virtual consumer has her own mental state and decision-making strategies to simulate her purchase decisions. The system of virtual consumers generates the global sales and a marketer can understand the rules that govern the market. A well-known paradigm to model these systems is agent-based modeling (ABM). In this manuscript we present an ABM where the brand preferences of the consumer agents are modeled using 2-tuple fuzzy linguistic variables. These variables represent the perceptions these consumers have on the different aspects or drivers every product available in the market has (e.g., price or quality). The product selection process of the agents is based on those perceptions and a utility maximization rule. This rule requires a fuzzy aggregation of the fuzzy linguistic perceptions about the products. Our proposal employs an ordered weighted average (OWA) to aggregate them. Our experiments show this approach does not suffer any loss of information when applied on data from real markets. Hence it is a suitable representation of the products preferences, normally represented by qualitative values in marketing surveys. To the best of our knowledge, this is the first work integrating a marketing ABM with fuzzy linguistic modeling.

Marketing professionals face challenges of increasing complexity to adapt classic marketing strategies to the phenomenon of social networks. Companies are currently trying to take advantage of the useful collective knowledge available on social networks to support different types of marketing decisions. The appropriate analysis of this information can offer marketing professionals with important competitive advantages. This work proposes a new methodology to extract the social collective behavior of Twitter users concerning a group of brands based on the users’ temporal activity. Time series of mentions made by individual users to each company's Twitter account are aggregated to obtain collective activity data for the companies, which is a consequence of both the company's and other users’ actions. These data are processed using classical unsupervised machine learning techniques, such as temporal clustering and hidden Markov models, to extract collective temporal behavior patterns and models of the dynamics of customers over time for a single brand and groups of brands. The derived knowledge can be used for different tasks, such as identifying the impact of a marketing campaign on Twitter and comparatively assessing the social behaviors of different brands and groups of brands to assist in making marketing decisions. Our methodology is validated in a case study from the wine market. Twitter data were gathered from four regions of different countries around the world with important wineries (Italy: Veneto, Portugal: Porto and Douro Valley, Spain: La Rioja, and United States: Napa Valley), and comparative behavior analysis was carried out from the perspective of the use of Twitter as a communication channel for marketing campaigns.

An appropriate visualization of multiobjective nondominated solutions is a valuable asset for decision making. Although there are methods for visualizing the solutions in the design space, they do not provide any information about their relationship. In this paper, we propose a novel methodology that allows the visualization of the nondominated solutions in the design space and their relationships by means of a network. The nodes represent the solutions in the objective space while the edges show the relationships among the solutions in the design space. Our proposal (called moGrams) thus provides a joint visualization of both objective and design spaces. It aims at helping the decision maker to get more understanding of the problem so that (s)he can choose the most appropriate and flexible final solution. moGrams can be applied to any multicriteria problem in which the solutions are related by a similarity metric. Besides, the decision maker interaction is facilitated by modifying the network based on the current preferences to obtain a clearer view. An exhaustive experimental study is performed using four multiobjective problems with a variable number of objectives to show both usefulness and versatility of moGrams. The results exhibit interesting characteristics of our methodology for visualizing and analyzing solutions of multiobjective problems.

Craniofacial superimposition (CFS) is a forensic identification technique, which studies the anatomical and morphological correspondence between a skull and a face. It involves the process of overlaying a variable number of facial images with the skull. This technique has great potential, since nowadays a wide majority of people have photographs, where their faces are clearly visible. In addition, the skull is a bone that hardly degrades under the effect of fire, humidity, temperature changes, and so on. Three consecutive stages for the CFS process have been distinguished: the acquisition and processing of the materials, the skull-face overlay, and the decision making. This final stage consists of determining the degree of support for a match based on the previous overlays. The final decision is guided by different criteria depending on the anatomical relations between the skull and the face. In previous approaches, we proposed a framework for automating this stage at different levels, taking into consideration all the information and uncertainty sources involved. In this paper, we model new anatomical skull-face regions and tackle the last level of the hierarchical decision support system. For the first time, we present a complete system, which provides a final degree of craniofacial correspondence. Furthermore, we validate our system as an automatic identification tool analyzing its capabilities in closed (known information or a potential list of those involved) and open lists (little or no idea at first who may be involved) and comparing its performance with the manual results achieved by experts, obtaining a remarkable performance. The proposed system has been demonstrated to be valid for sort-listing a given data set of initial candidates (in 62.5% of the cases, the positive one is ranked in the first position) and to serve as an exclusion method (97.4% and 96% of true negatives in training and test, respectively).

Craniofacial superimposition aims to identify a missing person by comparing its skull with photos of possible candidates. Among the difficult tasks involved, this requires superimposing the skull over each photo, matching the pose of the skull with that of the face, a problem known as skull-face overlay (SFO). Several computerized methods for SFO have been proposed, in an effort to relieve forensic experts of this complex, time-consuming, and subjective task. A system to generate artificial SFO data from computed tomography images has been also introduced, providing researchers with data to test and compare their techniques more reliably. This paper improves the state of the art on both fronts. We introduce a novel SFO algorithm that is substantially more accurate, more reliable, and much faster than the existing methods. An extensive experimental study and statistical analysis validates our findings. Moreover, we propose an improved method to simulate SFO data which, by replacing real photos with simulated ones, is able to generate a wider range of scenarios. This module provides complete control over the pose of the subject and the camera parameters, and even the ability to reproduce inter-expert errors in processing the input data, leading to a more controlled and thorough testing under realistic conditions.

Image registration (IR) is an extended and important problem in computer vision. It involves the transformation of different sets of image data having a shared content into a common coordinate system. Specifically, we will deal with the 3D intensity-based medical IR problem where the intensity distribution of the images is considered, one of the most complex and time consuming variants. The limitations of traditional IR methods have boomed the application of evolutionary and metaheuristic-based approaches to solve the problem, aiming to improve the performance of existing methods both in terms of accuracy and efficiency. In this contribution, we consider the use of a recently proposed bio-inspired meta-heuristic: the Coral Reef Optimization Algorithm (CRO). This novel algorithm simulates the natural phenomena underlying a coral reef, where different corals grow, reproduce and fight with other corals for space in the colony. CRO has recently obtained promising results in different real-world applications and we think its operation mode can properly cope with the 3D intensity-based medical IR problem. We adapt the algorithm to the real-coding problem nature and run an experimental setup tackling sixteen real-world problem instances. The new proposal is benchmarked with recent, state-of-the-art IR techniques. The results show that the CRO-based overcomes the state-of-the-art results in terms of its robustness and time efficiency.

The study of human remains suffers from a lack of information for determining a reliable estimation of the age of an individual. One of the most extended methods for this task was proposed in the twenties of the past century and is based on the analysis of the pubic bone. The method describes some age changes occurring in the pubic bone and establishes ten different age ranges with a description of the morphological aspect of the bone in each one of them. These descriptions are sometimes vague and there is not a systematic way for using the method. In this contribution we propose two different preliminary fuzzy rule-based classification system designs for age estimation from the pubic bone that consider the main morphological characteristics of the bone as independent and linguistic variables. So, we have identified the problem variables and we have defined the corresponding linguistic labels making use of forensic expert knowledge, that is also considered to design a decision support fuzzy system. A brief collection of pubic bones labeled by forensic anthropologists has been used for learning the second fuzzy rule-based classification system by means of a fuzzy decision tree. The experiments developed report a best performance of the latter approach.

In this paper, we will use agent-based modeling with the aim of simulating customer purchase processes in competitive market environments. These simulations will help to understand how customer behavior is affected by different social network topologies and the acquisition success of the products offered. We will extend a well-known agent-based model by incorporating an awareness customer behavior into it to make it closer to reality. In this way, individuals will not initially have a complete knowledge about all the products but they will gradually acquire it through a word-of-mouth process within the social network. Additionally, we will use genetic algorithms to generate automatic viral marketing strategies based on social network analysis metrics. We will compare the marketing results of the combined strategies and their impact based on different types of networks and the number of influential individuals. Finally, we will show that word-of-mouth evolves slower due to the awareness filter and that the genetic algorithm is able to find good solutions for targeting the most influential members of the market according to social network information.

Craniofacial superimposition is a forensic identification method involving the overlay of a skull over the available ante-mortem photographs of a candidate missing person face and the subsequent analysis of their anatomical correspondence. Within this process, the decision making stage focuses on determining the degree of support of being the same person or not based on the analysis of some criteria assessing the skull-face morphological correspondence. That decision is usually made in a non automatic and subjective way. We aim to automate the decision making process using computer vision and soft computing methods to assist the forensic anthropologist. In a previous study we have developed several methods to measure the matching of the correspondence between the face and the skull. The accuracy of each method was calculated as its capability to discriminate in a cross-comparison identification scenario. By the use of aggregation functions we can combine the results of the different methods taking into account the corresponding individual accuracy. This allows us to provide a single global output specifying the matching of each criterion while combining the capability of different methods. In this work, we present a study of the behavior of different aggregation functions for this aim. The performance of the aggregated methods has been tested on 172 skull-face overlay problem instances of positive and negative cases. The obtained results show that Sugeno integral ranks better than the counterparts although not significant conclusions can be delivered regarding the performance.

Diversity is a key characteristic of a classifier ensemble. A classifier ensemble must be composed of base classifiers with different performance in different areas of the problem space. Several works studied different diversity measures by performing extensive numerical experiments. However, up to our knowledge, no method has been proposed to visualize the diversity of a classifier ensemble. In this contribution, we propose a novel approach to visualize and analyze classifier ensembles diversity. Our novel proposal (called DivGrams), allows the visualization of the diversity between base classifiers in the classifier ensemble by using a network representation. The nodes represent base classifiers and its edges represent diversity relations between the base classifiers. We apply DivGrams to fuzzy rule-based classifier ensembles, as in our previous work these machine learning models proved to deal well with complex classification problems. A preliminary experimental study is performed using two UCI datasets in order to show the usefulness of DivGrams. Furthermore, we show a practical application of DivGrams to develop manual classifier selection on eight UCI datasets. The results obtained show the interesting characteristics of our proposal and support this novel approach in the fuzzy classifier ensemble field.

Image registration (IR) or image alignment is a fundamental step in medical image analysis when multiple images are involved. In most of such applications, the registration is performed following the intensity-based approach, which turns IR into a complex, computationally expensive, continuous optimization problem. In this paper, we introduce a new technique for intensity-based medical IR using the Bacterial Foraging Optimization Algorithm (BFOA), a novel bio-inspired metaheuristic. BFOA has recently obtained promising results in many real-world applications, including feature-based IR. The new algorithm is compared on a complex medical IR application against recent, outstanding IR techniques both traditional and based on meta-heuristics. The results show that our proposal is competitive with the state of the art, making BFOA a promising solution to tackle other complex, real-world optimization problems.

The advantages of multi-classification schemes based on decomposition strategies, and especially the One-vs-One framework, have been stressed even for those algorithms that can address multiple classes. However, there is an inherent hitch for the One-vs-One learning scheme related to the decision process: the non-competent classifier problem. This issue refers to the case where a binary classifier outputs a score degree for a couple of classes that are not related with the input example, thus including “noise” in the score-matrix and degrading the final accuracy. For this reason, several approaches have been developed in order to address the influence of the non-competence. Among them, the distance-based combination strategy has excelled as a very robust solution. In this contribution, we aim at investigating the behaviour of this approach using Evolutionary Fuzzy Systems as baseline classifiers. We will show that the synergy between both methodologies allows a significant improvement of the results to be obtained in contrast to the standard classifier and the classical One-vs-One scheme.

Craniofacial superimposition involves the process of overlaying a skull with a number of ante-mortem images of an individual and the analysis of their morphological correspondence. Within the craniofacial superimposition process, the skull-face overlay stage focuses on achieving the best possible overlay of the skull and a single ante-mortem image of a missing person. This technique has been commonly applied following a nonautomatic trial-and-error approach. Automatic skull-face overlay methods have been developed obtaining promising results. In this paper, we present two new variants that are an extension of existing 3-D-2-D methods to automatically superimpose a skull 3-D model on a facial photograph. We have modeled the imprecision related to the facial soft tissue depth between corresponding pairs of cranial and facial landmarks which typically guide the automatic approaches. As an illustration of the model's performance, the soft tissue distances associated to studies for Mediterranean population have been considered for dealing with this landmark matching uncertainty. Hence, we directly incorporate the corresponding landmark spatial relationships within the automatic skull-face overlay procedure. We have tested the performance of our proposal on 18 skull-face overlay instances from a ground truth data set obtaining valuable results. The current proposal is thus the first automatic skull-face overlay method evaluated in a reliable and unbiased way.

In a preceding contribution, we proposed a novel combination method by means of a fuzzy linguistic rule-based classification system. The fuzzy linguistic combination method was based on a genetic fuzzy system in order to learn its parameters from data. By doing so the resulting classifier ensemble was able to show a hierarchical structure and the operation of the latter component was transparent to the user. In addition, for the specific case of fuzzy classifier ensembles, the new approach allowed fuzzy classifiers to deal with high dimensional classification problems avoiding the curse of dimensionality. However, this approach strongly depended on one parameter defining the complexity of the final classifier ensemble and in consequence affecting the final accuracy. To avoid this tedious problem, we propose to automatically derive this parameter. For this purpose, we use the most common evolutionary multiobjective algorithm, namely NSGA-II, in order to optimize two criteria, complexity and accuracy. We carry out comprehensive experiments considering 20 UCI datasets with different dimensionality, showing the good performance of the proposed approach.

This paper is intended to verify that cost-sensitive learning is a competitive approach for learning fuzzy rules in certain imbalanced classification problems. It will be shown that there exist cost matrices whose use in combination with a suitable classifier allows for improving the results of some popular data-level techniques. The well known FURIA algorithm is extended to take advantage of this definition. A numerical study is carried out to compare the proposed cost-sensitive FURIA to other state-of-the-art classification algorithms, based on fuzzy rules and on other classical machine learning methods, on 64 different imbalanced datasets.

Fuzzy rule-based systems suffer from the so-called curse of dimensionality when applied to high complexity datasets, which consist of a large number of variables and/or examples. Fuzzy rule-based multiclassification systems have shown to be a good approach to deal with this kind of problems. In this contribution, we would like to take one step forward and extend this approach with random oracles with the aim that this fast and generic method induces more diversity and in this way improves the performance of the system. We will conduct exhaustive experiments considering 29 UCI and KEEL datasets with high complexity (considering both a number of attributes as well as a number of examples). The results obtained are promising and show that random oracles fuzzy rule-based multiclassification systems can be competitive with random oracles multiclassification systems using state-of-the-art base classifiers, when dealing with high complexity datasets.

Image registration (IR) is a challenging topic in both the computer vision and pattern recognition fields; its main aim is to find the optimal transformation to provide the best overlay or fitting between two or more images. Usually, the success of well-known algorithms, such as iterative closest point, highly depends on several assumptions, e.g., the user should provide an initial near-optimal pose of the images to be registered. In the last decade, a new family of registration algorithms based on evolutionary principles has been contributed in order to overcome the latter drawbacks. However, their performance highly depends on carefully tuning (usually by hand) the control parameters of the algorithm, which is an error-prone and a time-consuming task. In this paper, we propose a new self-adaptive evolution model to deal with IR problems. To our knowledge, this is the first time a self-adaptive approach has been used for tuning the control parameters of evolutionary algorithms tackling computer vision tasks. Specifically, we introduce a novel design of the proposed self-adaptive approach facing pair-wise range IR problem instances, which is a challenging real-world optimization problem. In addition, several classical approaches, as well as state-of-the-art evolutionary IR methods, have been considered for numerical comparison.

Since Zadeh’s proposal and Mamdani’s seminal ideas, interpretability is acknowledged as one of the most appreciated and valuable characteristics of fuzzy system identification methodologies. It represents the ability of fuzzy systems to formalize the behavior of a real system in a human understandable way, by means of a set of linguistic variables and rules with a high semantic expressivity close to natural language. Interpretability analysis involves two main points of view: readability of the knowledge base description (regarding complexity of fuzzy partitions and rules) and comprehensibility of the fuzzy system (regarding implicit and explicit semantics embedded in fuzzy partitions and rules, as well as the fuzzy reasoning method). Readability has been thoroughly treated by many authors who have proposed several criteria and metrics. Unfortunately, comprehensibility has usually been neglected because it involves some cognitive aspects related to human reasoning, which are very hard to formalize and to deal with. This paper proposes the creation of a new paradigm for fuzzy system comprehensibility analysis based on fuzzy systems’ inference maps, so-called fuzzy inference-grams (fingrams), by analogy with scientograms used for visualizing the structure of science. Fingrams show graphically the interaction between rules at the inference level in terms of co-fired rules, i.e., rules fired at the same time by a given input. The analysis of fingrams offers many possibilities: measuring the comprehensibility of fuzzy systems, detecting redundancies and/or inconsistencies among fuzzy rules, identifying the most significant rules, etc. Some of these capabilities are explored in this study for the case of fuzzy models and classifiers.

Polygonal surface models are typically used in three-dimensional (3D) visualizations and simulations. They are obtained by laser scanners, computer vision systems or medical imaging devices to model highly detailed object surfaces. Surface mesh simplification aims to reduce the number of faces used in a 3D model while keeping the overall shape, boundaries, and volume. In this work, we propose to deal with the mesh simplification problem from an evolutionary multi-objective viewpoint. The quality of a solution is defined by two conflicting objectives: the accuracy and the simplicity of the model. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is adapted to tackle the problem. We compare the NSGA-II performance with a classical approach and a single-objective implementation. The comparison has been carried out using different datasets.

Scientograms are graph representations of scientific information. Exploring vast amount of scientograms for scientific data analysis has been of great interest in Information Science. This work emphasizes the application of multiobjective subgraph mining for the scientogram analysis task regarding the extraction of common research areas in the world. For this task, we apply a recently proposed multiobjective Subdue (MOSubdue) algorithm for frequent subgraph mining in graph-based data. The algorithm incorporates several ideas from evolutionary multiobjective optimization. The underlying scientogram structure is a social network, i.e., a graph, MOSubdue can uncover common (or frequent) scientific structures to different scientograms. MOSubdue performs scientogram mining by jointly maximizing two objectives, the support (or frequency) and complexity of the mined scientific structures. Experimental results on five realworld datasets from Elsevier-Scopus scientific database clearly demonstrated the potential of multiobjective subgraph mining in scientogram analysis.

Human gait modeling consists of studying the biomechanics of this human movement. Its importance lies in the fact that its analysis can help in the diagnosis of walking and movement disorders or rehabilitation programs, among other medical situations. Fuzzy finite state machines can be used to model the temporal evolution of this type of phenomenon. Nevertheless, the definition of details of the model in each particular case is a complex task for experts. In this paper, we present an automatic method to learn the model parameters that are based on the hybridization of fuzzy finite state machines and genetic algorithms leading to genetic fuzzy finite state machines. This new genetic fuzzy system automatically learns the fuzzy rules and membership functions of the fuzzy finite state machine, while an expert defines the possible states and allowed transitions. Our final goal is to obtain a specific model for each person's gait in such a way that it can generalize well with different gaits of the same person. The obtained model must become an accurate and human friendly linguistic description of this phenomenon, with the capability to identify the relevant phases of the process. A complete experimentation is developed to test the performance of the new proposal when dealing with datasets of 20 different people, comprising a detailed analysis of results, which shows the advantages of our proposal in comparison with some other classical and computational intelligence techniques.

The time and space assembly line balancing problem (TSALBP) considers realistic multiobjective versions of the classical assembly line balancing involving the joint optimization of conflicting criteria such as the cycle time, the number of stations, and/or the area of these stations. This industrial problem is very difficult to solve and of crucial importance in the manufacturing context. As TSALBP-1/3 contains a set of hard constraints like precedences or cycle time limits for each station it has been mainly tackled using multiobjective constructive metaheuristics (e.g. ant colony optimization). Global search algorithms in general -and multiobjective genetic algorithms in particular have shown to be ineffective to solve this family of problems up to now. The goal of this contribution is to present a new multiobjective genetic algorithm design, taking the well known NSGA-II algorithm as a base and new coding scheme and specific operators, to properly tackle with the TSALBP. An experimental study on six different problem instances is used to compare the proposal with the state-of-the-art methods.

This work proposes multiobjective subgraph mining in graph-based data using multiobjective evolutionary programming (MOEP). A mined subgraph is defined by two objectives, support and size. These objectives are conflicting as a subgraph with high support value is usually of small size and vice-versa. MOEP applies NSGA-II's nondominated sorting procedure to evolve the population during the subgraph generation process. An experimental study on five synthetic and real-life graph-based datasets shows that MOEP outperforms Subdue-based methods, a well-known heuristic search approach for subgraph discovery in data mining community. The comparison is done using hypervolume, C and Ie multiobjective performance metrics.

Subgraph Mining aims to find frequent, descriptive and interesting subgraphs in a graph database. Usually, this search involves simple user-defined thresholds and is only driven by a single-objective. In this paper, we propose an Evolutionary Multiobjective Optimization algorithm, called MOEP-SO, to mine subgraphs from graph-represented data by maximizing two objectives, support and size of the subgraphs. Experimental results on synthetic and real-life graph-based datasets validate the utility of the proposed methodology when benchmarked against classical single-objective methods and their previous, non-evolutionary multiobjective extensions.

In this work, we conduct a study considering a fuzzy rule-based multiclassification system design framework based on Fuzzy Unordered Rule Induction Algorithm (FURIA). This advanced method serves as the fuzzy classification rule learning algorithm to derive the component classifiers considering bagging and feature selection. We develop an exhaustive study on the potential of bagging and feature selection to design a final FURIA-based fuzzy multiclassifier dealing with high dimensional data. Several parameter settings for the global approach are tested when applied to twenty one popular UCI datasets. The results obtained show that FURIA-based fuzzy multiclassifiers outperform the single FURIA classifier and are competitive with C4.5 multiclassifiers and random forests.

Craniofacial superimposition (CS) is a forensic process where photographs or video shots of a missing person are compared with the skull that is found. By projecting both photographs on top of each other (or, even better, matching a scanned 3-D skull model against the face photo/video shot), the forensic anthropologist can try to establish whether it is the same person. The whole process is influenced by inherent uncertainty, mainly because two objects of different nature (a skull and a face) are involved. In this paper, we extend our previous evolutionary-algorithm-based method to automatically superimpose the 3-D skull model and the 2-D face photo with the aim to overcome the limitations that are associated with the different sources of uncertainty, which are present in the problem. Two different approaches to handle the imprecision will be proposed: weighted and fuzzy-set-theory-based landmarks. The performance of the new proposal is analyzed, considering five skull–face overlay problem instances that correspond to three real-world cases solved by the Physical Anthropology Laboratory, University of Granada, Granada, Spain. The experimental study that is developed shows how the fuzzy-set-based approach clearly outperforms the previous crisp solution. Finally, the proposed method is validated by the comparison of its outcomes with respect to those manually achieved by the forensic experts in nine skull–face overlay problem instances.

Image registration is a very active research area in computer vision. Image registration methods, aim to find a transformation between two images taken under different conditions. Point matching is an image registration approach based on searching for the right pairing of points between the two images. From this matching, the registration transformation can be inferred by means of numerical methods. In this paper, we tackle the medical image registration problem adapting a new advanced hybrid metaheuristic composed by the GRASP and the evolutionary path relinking algorithms, called G&EvPR. The experiments conducted in this work have shown the good performance of G&EvPR compared to similar approaches of the state of the art when dealing with different medical image modalities. In particular, a good tradeoff between search space diversification and intensification is achieved.

In this work we propose a Pareto-based multi-objective search strategy for subgraph mining in structural databases. The method is an extension of Subdue, a classical graph-based knowledge discovery algorithm, and it is thus called MultiObjective Subdue (MOSubdue). MOSubdue incorporates the NSGA-II's crowding selection mechanism in order to retrieve a well distributed Pareto optimal set of meaningful subgraphs showing different optimal trade-offs between support and complexity, in a single run. The good performance of the proposed approach is empirically demonstrated by using a reallife data set concerning the analysis of web sites.

A preliminary study combining two diversity measures with an accuracy measure in two bicriteria fitness functions to genetically select fuzzy rule-based multiclassification systems is conducted in this paper. The fuzzy rule-based classification system ensembles are generated by means of bagging and mutual information-based feature selection. Several experiments were developed using four popular UCI datasets with different dimensionality in order to analyze the accuracy-complexity trade-off obtained by a genetic algorithm considering the two fitness functions. Comparison are made with the initial fuzzy ensemble and a single fuzzy classifier.

In this contribution we explore the combination of bagging with random subspace and two variants of Battiti's mutual information feature selection methods to design fuzzy rule-based classification system ensembles. Besides, we consider a multicriteria genetic algorithm guided by the training error to select the component classifiers, in order to look for appropriate accuracy-complexity trade-offs in the final multiclassifier.

Scatter search is a population-based method that has recently been shown to yield promising outcomes for solving combinatorial and nonlinear optimization problems. Based on formulations originally proposed in the 1960s for combining decision rules and problem constraints, such as the surrogate constraint method, scatter search uses strategies for combining solution vectors that have proved effective in a variety of problem settings. We present a scatter-search implementation designed to find high-quality solutions for the 3D image-registration problem, which has many practical applications. This problem arises in computer vision applications when finding a correspondence or transformation between two computer images obtained under different conditions. Our implementation goes beyond a simple exercise on applying scatter search, by incorporating innovative mechanisms to combine and improve solutions and to create a balance between intensification and diversification in the reference set. Furthermore, heuristic information taken from a preprocessing of the images is incorporated into the algorithm to improve its performance. Our computational experimentation tackling two different medical registration applications establishes the effectiveness of scatter search in relation to different approaches usually applied to solving the problem. We have considered both simulated magnetic resonance images and real-world computerized tomography images as data sets. To measure the robustness of our proposal, the image data sets are intentionally selected for addressing registration environments with the presence of noise, anatomical lesions, and occlusions between images.

Abstract Network scaling algorithms such as the Pathfinder algorithm are used to prune many different kinds of networks, including citation networks, random networks, and social networks. However, this algorithm suffers from run time problems for large networks and online processing due to its O(n4) time complexity. In this article, we introduce a new alternative, the MST‐Pathfinder algorithm, which will allow us to prune the original network to get its PFNET(∞, n − 1) in just O(n2 · log n) time. The underlying idea comes from the fact that the union (superposition) of all the Minimum Spanning Trees extracted from a given network is equivalent to the PFNET resulting from the Pathfinder algorithm parameterized by a specific set of values (r = ∞ and q = n − 1), those usually considered in many different applications. Although this property is well‐known in the literature, it seems that no algorithm based on it has been proposed, up to now, to decrease the high computational cost of the original Pathfinder algorithm. We also present a mathematical proof of the correctness of this new alternative and test its good efficiency in two different case studies: one dedicated to the post‐processing of large random graphs, and the other one to a real world case in which medium networks obtained by a cocitation analysis of the scientific domains in different countries are pruned.

Current tools and techniques devoted to examine the content of large databases are often hampered by their inability to support searches based on criteria that are meaningful to their users. These shortcomings are particularly evident in data banks storing representations of structural data such as biological networks. Conceptual clustering techniques have demonstrated to be appropriate for uncovering relationships between features that characterize objects in structural data. However, typical conceptual clustering approaches normally recover the most obvious relations, but fail to discover the less frequent but more informative underlying data associations. The combination of evolutionary algorithms with multiobjective and multimodal optimization techniques constitutes a suitable tool for solving this problem. We propose a novel conceptual clustering methodology termed evolutionary multiobjective conceptual clustering (EMO-CC), relying on the NSGA-II multiobjective (MO) genetic algorithm. We apply this methodology to identify conceptual models in structural databases generated from gene ontologies. These models can explain and predict phenotypes in the immunoinflammatory response problem, similar to those provided by gene expression or other genetic markers. The analysis of these results reveals that our approach uncovers cohesive clusters, even those comprising a small number of observations explained by several features, which allows describing objects and their interactions from different perspectives and at different levels of detail.

One of the main advantages of fuzzy systems is their ability to design comprehensible models of real-world systems, thanks to the use of a fuzzy rule structure easily interpretable by human beings. This is especially useful for the design of fuzzy logic controllers, where the knowledge base can be extracted from expert knowledge. Even more, the availability of a readable structure allows the human expert to customize the fuzzy controller to different environments by manually tuning its components. Nevertheless, this tuning task is usually a time-consuming procedure when done manually, especially when several measures are considered to evaluate the controller performance, and thus the interest in the design of automatic tuning procedures for fuzzy systems has increased along the last few years. In this paper, we tackle the tuning of the fuzzy membership functions of a fuzzy visual system for autonomous robots. This fuzzy visual system is based on a hierarchical structure of three different fuzzy classifiers, whose combined action allows the robot to detect the presence of doors in the images captured by its camera. Although the global knowledge represented in the fuzzy system knowledge base makes it perform properly in the door detection task, its adaptation to the specific conditions of the environment where the robot is operating can significantly improve the classification accuracy. However, the tuning procedure is complex as two different performance indexes are involved in the optimization process (true positive and false positive detections), thus becoming a multiobjective problem. Hence, in order to automatically put the fuzzy system tuning into effect, different single and multiobjective evolutionary algorithms are considered to optimize the two criteria, and their behavior in problem solving is compared.

Craniofacial superimposition is a process that aims to identify a person by overlaying a photograph and a model of the skull. This process is usually carried out manually by forensic anthropologists; thus being very time consuming and presenting several difficulties in finding a good fit between the 3D model of the skull and the 2D photo of the face. In this paper we present a fast and automatic procedure to tackle the superimposition problem. The proposed method is based on real-coded genetic algorithms. Synthetic data are used to validate the method. Results on a real case from our Physical Anthropology lab of the University of Granada are also presented.

This work shows how to achieve a good interpretability-accuracy trade-off through keeping the strong fuzzy partition property along the whole fuzzy modeling process. First, a small compact knowledge base is built. It is highly interpretable and reasonably accurate. Second, an optimization procedure, which only affects the fuzzy partitions defining the system variables, is carried out. It improves the system accuracy while preserving the system interpretability. Two optimization strategies are compared: Solis-Wetts, a local search based strategy; and Genetic Tuning, a global search based strategy. Results obtained in a well-known benchmark medical classification problem, related to breast cancer diagnosis, show that our methodology is able to achieve knowledge bases with high interpretability and accuracy comparable to that obtained by other methodologies.

This work presents the use of local fuzzy prototypes as a new idea to obtain accurate local semantics-based Takagi–Sugeno–Kang (TSK) rules. This allow us to start from prototypes considering the interaction between input and output variables and taking into account the fuzzy nature of the TSK rules. To do so, a two-stage evolutionary algorithm based on MOGUL (a methodology to obtain Genetic Fuzzy Rule-Based Systems under the Iterative Rule Learning approach) has been developed to consider the interaction between input and output variables. The first stage performs a local identification of prototypes to obtain a set of initial local semantics-based TSK rules, following the Iterative Rule Learning approach and based on an evolutionary generation process within MOGUL (taking as a base some initial linguistic fuzzy partitions). Because this generation method induces competition among the fuzzy rules, a postprocessing stage to improve the global system performance is needed. Two different processes are considered at this stage, a genetic niching-based selection process to remove redundant rules and a genetic tuning process to refine the fuzzy model parameters. The proposal has been tested with two real-world problems, achieving good results. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 909–941, 2007.

The four papers in this special section address distinct subjects focusing on new, significant novel lines of development on genetic fuzzy systems (GFSs).

Different tasks in forensic anthropology require the use of three-dimensional models of forensic objects. Since range scanners do not allow to capture the whole object in a single image, multiple scans from different views are needed to supply the information to construct the 3D model. Range image registration methods study the accurate integration of the different views acquired by these scanners. Specifically, pair-wise image registration methods manage every adjacent pair of scanned views. Our proposal is based on the adaptation of a previous work in order to apply the scatter search evolutionary algorithm to pair-wise image registration in forensic anthropology applications. To measure the performance of this adaptation, we design an experimental setup considering some of the most recent and accurate evolutionary techniques for the problem to compose a 3D model of one skull from our Physical Anthropology Lab.

Image registration is a crucial task in many areas such as computer vision, neurosurgery, remote sensing, and cartography. Different approaches to solve the problem have been proposed in the specialized literature but present some drawbacks. In this contribution, we focus our interest on the 3D surface IR problem and our proposal considers the use of an emergent evolutionary global optimization strategy called scatter search, providing a fast and accurate algorithm to estimate similarity transformations. To measure its performance, we make a broad comparison with some of the most accepted and accurate classical and evolutionary techniques. The new proposal will be validated using two different surfaces, a synthetic and a magnetic resonance image, considered by other researchers in the field, both in noise-free and noisy scenarios.

Tuning fuzzy rule-based systems for linguistic fuzzy modeling is an interesting and widely developed task. It involves adjusting some of the components of the knowledge base without completely redefining it. This contribution introduces a genetic tuning process for jointly fitting the fuzzy rule symbolic representations and the meaning of the involved membership functions. To adjust the former component, we propose the use of linguistic hedges to perform slight modifications keeping a good interpretability. To alter the latter component, two different approaches changing their basic parameters and using nonlinear scaling factors are proposed. As the accomplished experimental study shows, the good performance of our proposal mainly lies in the consideration of this tuning approach performed at two different levels of significance. The paper also analyzes the interaction of the proposed tuning method with a fuzzy rule set reduction process. A good interpretability-accuracy tradeoff is obtained combining both processes with a sequential scheme: first reducing the rule set and subsequently tuning the model.

Persistent queries are a specific kind of queries used in information retrieval systems to represent a user's long-term standing information need. These queries can present many different structures, being the "bag of words" that most commonly used. They can be sometimes formulated by the user, although this task is usually difficult for him and the persistent query is then automatically derived from a set of sample documents he provides. In this work we aim at getting persistent queries with a more representative structure for text retrieval issues. To do so, we make use of soft computing tools: fuzzy logic is considered for representation and inference purposes by dealing with the extended Boolean query structure, and multiobjective evolutionary algorithms are applied to build the persistent fuzzy query. Experimental results show how both an expressive fuzzy logic-based query structure and a proper learning process to derive it are needed in order to get a good retrieval efficacy, when comparing our process to single-objective evolutionary methods to derive both classic Boolean and extended Boolean queries.

In our opinion, there are two main concerns in Roubos and Babugka's note, that are summarized as follows. 1) The kinds of problems used in our paper to test the algorithm proposed in "A proposal to improve the accuracy of linguistic modeling" and other studies. The authors claim that they are very simple to be considered as benchmarks for nonlinear modeling techniques. 2) The interpretability of the different kinds of models considered. Roubos and Babuska think that there is no difference between the interpretability of fuzzy linguistic models, Takagi-Sugeno-Kang (TSK) fuzzy models, and mathematical formulations (linear models, in this case). We agree with some of the opinions of the authors of the note but not with some others.

In this paper, we propose an approach to design linguistic models which are accurate to a high degree and may be suitably interpreted. This approach is based on the development of a hierarchical system of linguistic rules learning methodology. This methodology has been thought as a refinement of simple linguistic models which, preserving their descriptive power, introduces small changes to increase their accuracy. To do so, we extend the structure of the knowledge base of fuzzy rule base systems in a hierarchical way, in order to make it more flexible. This flexibilization will allow us to have linguistic rules defined over linguistic partitions with different granularity levels, and thus to improve the modeling of those problem subspaces where the former models have bad performance.

This paper introduces a new learning methodology to quickly generate accurate and simple linguistic fuzzy models: the cooperative rules (COR) methodology. It acts on the consequents of the fuzzy rules to find those that are best cooperating. Instead of selecting the consequent with the highest performance in each fuzzy input subspace, as ad-hoc data-driven methods usually do, the COR methodology considers the possibility of using another consequent, different from the best one, when it allows the fuzzy model to be more accurate thanks to having a rule set with the best cooperation. Our proposal has shown good results in solving three different applications when compared to other methods.

A method is proposed to automatically learn the knowledge base by finding an appropiate data base by means of a genetic algorithm while using a simple generation method to derive the rule base. Our genetic process learns the number of linguistic terms per variable and the membership function parameters that define their semantics, while a rule base generation method learns the number of rules and their composition.

We propose accurate linguistic modeling, a methodology to design linguistic models that are accurate to a high degree and may be suitably interpreted. This approach is based on two main assumptions related to the interpolative reasoning developed by fuzzy rule-based systems: a small change in the structure of the linguistic model based on allowing the linguistic rule to have two consequents associated; and a different way to obtain the knowledge base based on generating a preliminary fuzzy rule set composed of a large number of rules and then selecting the subset of them best cooperating. Moreover, we introduce two variants of an automatic design method for these kinds of linguistic models based on two well-known inductive fuzzy rule generation processes and a genetic process for selecting rules. The accuracy of the proposed methods is compared with other linguistic modeling techniques with different characteristics when solving of three different applications.

The main aim of this paper is to present MOGUL, a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach. MOGUL will consist of some design guidelines that allow us to obtain different genetic fuzzy rule-based systems, i.e., evolutionary algorithm-based processes to automatically design fuzzy rule-based systems by learning and/or tuning the fuzzy rule base, following the same generic structure and able to cope with problems of a different nature. A specific evolutionary learning process obtained from the paradigm proposed to design unconstrained approximate Mamdani-type fuzzy rule-based systems will be introduced, and its accuracy in the solving of a real-world electrical engineering problem will be analyzed. ©1999 John Wiley & Sons, Inc.

Nowadays, fuzzy rule-based systems are successfully applied to many different real-world problems. Unfortunately, relatively few well-structured methodologies exist for designing and, in many cases, human experts are not able to express the knowledge needed to solve the problem in the form of fuzzy rules. Takagi-Sugeno-Kang (TSK) fuzzy rule-based systems were enunciated in order to solve this design problem because they are usually identified using numerical data. In this paper we present a two-stage evolutionary process for designing TSK fuzzy rule-based systems from examples combining a generation stage based on a (mu, lambda)-evolution strategy, in which the fuzzy rules with different consequents compete among themselves to form part of a preliminary knowledge base, and a refinement stage in which both the antecedent and consequent parts of the fuzzy rules in this previous knowledge base are adapted by a hybrid evolutionary process composed of a genetic algorithm and an evolution strategy to obtain the final Knowledge base whose rules cooperate in the best possible way. Some aspects make this process different from others proposed until now: the design problem is addressed in two different stages, the use of an angular coding of the consequent parameters that allows us to search across the whole space of possible solutions, and the use of the available knowledge about the system under identification to generate the initial populations of the Evolutionary Algorithms that causes the search process to obtain good solutions more quickly. The performance of the method proposed is shown by solving two different problems: the fuzzy modeling of some three-dimensional surfaces and the computing of the maintenance costs of electrical medium line in Spanish towns. Results obtained are compared with other kind of techniques, evolutionary learning processes to design TSK and Mamdani-type fuzzy rule-based systems in the first case, and classical regression and neural modeling in the second.

In this paper, we present a multistage genetic learning process for obtaining linguistic fuzzy rule-based classification systems that integrates fuzzy reasoning methods cooperating with the fuzzy rule base and learns the best set of linguistic hedges for the linguistic variable terms. We show the application of the genetic learning process to two well known sample bases, and compare the results with those obtained from different learning algorithms. The results show the good behavior of the proposed method, which maintains the linguistic description of the fuzzy rules. © 1998 John Wiley & Sons, Inc.