Abstract Tropospheric ozone forecasting is critical for public health, yet the deep learning models that achieve high accuracy often function as black boxes. This lack of transparency, along with the inability of popular explainability techniques like SHapley Additive exPlanations (SHAP) to capture essential temporal dependencies, limits their practical utility and trustworthiness in environmental management. To address this, we propose a novel framework, eXplainable Deep Learning with Spectral Co-clustering for Time Series, that integrates spectral co-clustering to enhance forecasting performance and provide post-hoc structured interpretability for air-quality time series. The methodology comprises data preprocessing, feature engineering (including lagging, rolling statistics, and time-based features), and deep learning architectures (Multilayer Perceptron, Gated Recurrent Unit, and hybrid models). Bayesian optimization is used to fine-tune hyperparameters. The core contribution is a spectral co-clustering technique that simultaneously partitions features and time instances into co-clusters, revealing critical inter-feature relationships and temporal patterns that drive predictions. The framework was rigorously validated through extensive experiments on data from five air quality monitoring stations. The proposed approach achieved RMSE values ranging from 0.73 to 6.08, significantly outperforming existing methods, including a temporal LSTM baseline, with performance improvements of approximately 59.19% to 95.65%. Results demonstrate that the proposed approach not only achieves high forecasting accuracy but also, through post-hoc heatmap visualizations of the objectively selected best-performing co-cluster, identifies the key features and time periods governing model predictions, thereby offering an interpretable understanding of the temporal and feature-level drivers associated with ozone variability. Thus, a transparent and effective solution for ozone forecasting is proposed, with a modular design generalizable to other environmental time series prediction tasks.

In the past few years, Federated Learning (FL) has emerged as an effective approach for training Neural Networks (NNs) over a computing network while preserving data privacy. Most existing FL approaches require defining a priori (1) a predefined structure for all the NNs running on the clients and (2) an explicit aggregation procedure. These can be limiting factors in cases where predefining such algorithmic details is difficult. Recently, NEvoFed was proposed, an FL method that leverages Neuroevolution running on the clients, in which the NN structures are heterogeneous and the aggregation is implicitly accomplished on the client side. Here, we propose MFC-NEvoFed, a novel approach to FL that does not require learning models, i.e., neural network parameters, to be distributed over the networks, thus taking a step toward security improvement. The only information exchanged in client/server communication is the performance of each model on local data, allowing the emergence of optimal NN architectures without needing any kind of model aggregation. Another appealing feature of our framework is that it can be used with any Machine Learning algorithm provided that, during the learning phase, the model updates do not depend on the input data. To assess the validity of MFC-NEvoFed, we test it on four datasets, showing that very compact NNs can be obtained without drops in performance compared to canonical FL. Finally, such compact structures allow for a step toward explainability, which is highly desirable in domains such as digital health, from which the tested datasets come.

Effective and efficient disease detection is crucial, particularly for economically important crops like cotton.In this paper, we move from the initial development of a deep-learning model for cotton leaf disease detection, called Deep-CCNet, to a more comprehensive comparison of different feature selection algorithms, such as RainWater Algorithm, Particle Swarm Optimization, Bee Evolutionary Algorithm, Genetic Algorithm, and Binary Dragonfly Algorithm.Although Deep-CCNet achieved satisfactory classification performance, the goal of this study is to improve the classification performance and efficiency of deep learning models with meta-heuristic feature selection techniques.This study aims to determine which feature selection method achieves the best balance between performance and computational efficiency.We used the Kaggle "cotton leaf disease dataset", which has 1,711 images from four classes (namely curl virus, bacterial blight, fusarium wilt, and healthy leaf images), to compare these techniques systematically.Our research attempts to find the most effective method that maximizes model performance while minimizing computing resources, in addition to benchmarking the computational and performance parameters of each approach.The results of this study provide a new approach for the choice of feature selection methods in plant pathology, leading to better early disease diagnosis and increased crop resilience via efficient farming practices.

This work examines the combined effect of segmentation and data augmentation, two key preprocessing strategies often studied separately, on AI model classification performance and explainability. Three key experiments are conducted. First, the modified MobileNetV2 is applied to 21,165 raw images from the COVID-19 Radiography Database. While classification results are strong, Grad-CAM explanations misfocus on areas below the chest. Second, U-Net segmentation crops chest regions, and applying rotation, flipping, and brightness adjustment achieves a balance between accuracy and explainability. Third, precise cropping using U-Net segmentation masks isolates chest areas but slightly degrades classifier performance without further explainability gains. Findings suggest that combining U-Net segmentation with augmentation enhances explainability while maintaining model precision for COVID-19 detection. Their integration offers a trade-off between accuracy and explainability, reinforcing their complementary role in medical image analysis.

Artificial intelligence and deep learning are powerful tools for extracting knowledge from large datasets, particularly in healthcare. However, their black-box nature raises interpretability concerns, especially in high-stakes applications. Existing eXplainable Artificial Intelligence methods often focus solely on visualization or rule-based explanations, limiting interpretability's depth and clarity. This work proposes a novel explainable AI method specifically designed for medical image analysis, integrating statistical, visual, and rule-based explanations to improve transparency in deep learning models. Statistical features are derived from deep features extracted using a custom Mobilenetv2 model. A two-step feature selection method - zero-based filtering with mutual importance selection - ranks and refines these features. Decision tree and RuleFit models are employed to classify data and extract human-readable rules. Additionally, a novel statistical feature map overlay visualization generates heatmap-like representations of three key statistical measures (mean, skewness, and entropy), providing both localized and quantifiable visual explanations of model decisions. The proposed method has been validated on five medical imaging datasets - COVID-19 radiography, ultrasound breast cancer, brain tumor magnetic resonance imaging, lung and colon cancer histopathological, and glaucoma images - with results confirmed by medical experts, demonstrating its effectiveness in enhancing interpretability for medical image classification tasks.

In the past few years, Federated Learning (FL) has emerged as an effective approach for training neural networks (NNs) over a computing network while preserving data privacy. Most of the existing FL approaches require the user to define a priori the same structure for all the NNs running on the clients, along with an explicit aggregation procedure. This can be a limiting factor in cases where pre-defining such algorithmic details is difficult. To overcome these issues, we propose a novel approach to FL, which leverages Neuroevolution running on the clients. This implies that the NN structures may be different across clients, hence providing better adaptation to the local data. Furthermore, in our approach, the aggregation is implicitly accomplished on the client side by exploiting the information about the models used on the other clients, thus allowing the emergence of optimal NN architectures without needing an explicit aggregation. We test our approach on three datasets, showing that very compact NNs can be obtained without significant drops in performance compared to canonical FL. Moreover, we show that such compact structures allow for a step towards explainability, which is highly desirable in domains such as digital health, from which the tested datasets come.

The use of Super-Resolution SR algorithms applied to Magnetic Resonance Images (MRIs) is increasingly common in the medical field. Increasing the resolution of images allows physicians to more easily observe image details. Over the years, several SR approaches have been tried by researchers. Among the various approaches, Diffusion Models (DMs) have been shown to perform well in the SR task. In this work, we propose the use of a Latent Diffusion Model (LDM) for the SR of medical images. Different studies have shown that LDMs improve the performance of DMs in several SR tasks. To our knowledge, LDMs have not been tested for SR of medical images such as MRIs. We therefore perform fine-tuning of an LDM on medical datasets. To evaluate the SR images generated by the LDM, we compare them to the original high-resolution images using two similarity measurements. We show that the LDM achieves better similarity values than other SR models on the same medical dataset. We also show with visual examples the advantage of applying SR using an LDM.

This research presents DeepCRINet, a deep learning (DL) model designed for reliable performance across various Chest Radiography Images (CRIs) datasets, in response to the urgent need for quick and accurate lung disease identification utilizing CRIs. Our method builds on earlier research, which frequently used single-source datasets that might not adequately represent the heterogeneity present in clinical situations. Our model’s diagnostic adaptability and real-world dependability are improved by utilizing images from different datasets, which helps us overcome limitations such as dataset bias, robustness, generalizability, and underrepresentation of conditions. With validation on a broad dataset consisting of 14,096 images (from three different datasets), DeepCRINet provides a solution that demonstrates excellent flexibility in recognizing illnesses including TuBerculosis, Pneumonia, COVID-19, and Lung Opacity. Through data augmentation, we improve the dataset, supporting training and testing procedures and confirming the model’s ability to generalize. We used occlusion sensitivity as a kind of explainable AI to openly identify and visually emphasize regions important to proper classification. This ability not only shows that DeepCRINet is analytically better than other DL models and hybrid techniques, but it also improves patient outcomes and diagnosis, which makes it a vital tool for medical professionals like radiologists.

Colon and Lung cancers are two of the most common causes of mortality in adults. They may simultaneously form in organs and have a detrimental effect on human life. There is a high risk that cancer will spread to the two organs if it is not discovered in the early stages. One of the most essential elements of successful therapy is the histological diagnosis of such cancers. Deep learning algorithms have improved the speed and accuracy of time-consuming and challenging procedures, enabling researchers to examine a huge number of patients swiftly and inexpensively. By examining their histological images and applying modern deep learning, this study develops a classification framework called DeepLCCNet to discriminate between five kinds of colon and lung tissues (three malignant and two benign). More precisely, we have classified five tissue types of Lung and Colon Cancer Histopathological Images data set using our model, i.e., benign tissue of the lung, squamous cell carcinoma of the lung, adenocarcinoma of the lung, benign tissue of the colon, and adenocarcinoma of the colon. According to the results, the proposed model can detect cancer tissues with an average accuracy of 99.67% and maximum accuracy of 99.84%. Medical professionals will be able to utilize a precise, automated system for detecting and classifying various kinds of colon and lung cancers.

Within this paper a Grammatical Evolution al-gorithm is exploited to induce personalized and interpretable glucose forecasting models for diabetic patients based on the historical measurements of the glucose, the carbohydrates, and the injected insulin. A real-world data set of Type 1 diabetic patients is used to assess the induced models. The experimental trials show that the performance of extracted models is compara-ble with that obtained by other state-of-the-art techniques that require a more significant computational effort.

The quality of life of diabetic patients can be enhanced by devising a personalized control algorithm, integrated within an artificial pancreas, capable of dosing the insulin. A key action in the building of this artificial device is to conceive an efficient algorithm for forecasting future glucose levels. Within this paper, an evolutionary-based strategy, i.e., a Grammatical Evolution algorithm, is devised to deduce a personalized forecasting model to evaluate blood glucose values in the future on the basis of the past glucose measurements, and the knowledge of the basal and infused insulin levels and of the food consumption. The aim is to discover models that are not only interpretable but also with low complexity to be used within a control algorithm that is the main element of the artificial pancreas. A real-world database composed by Type 1 diabetic patients has been employed to evaluate the proposed evolutionary automatic procedure.

The papers in this special focus on enabling technologies for next generation telehealthcare applications. The use of Information and Communication Technology (ICT) for health and well-being is rapidly increasing in the majority of high-income countries. The interest about telehealthcare allows the provisioning of various kinds of health-related services and applications over the Internet. There are several benefits associated with tele-healthcare, including: the reduction of infection risk due to optimized patients access to clinical centers; optimized healthcare workflows; containment of hospital costs; increased patient safety; improves in the quality of life of both patients and their families. Common telehealthcare applications include tele-nursing, tele-rehabilitation, tele-dialog, tele-monitoring, tele-analysis, tele-pharmacy, tele-care, tele-psychiatry, tele-radiology, tele-pathology, teledermatology, tele-dentistry, tele-audiology, tele-ophthalmology, etc. In the past ten years, key enabling technologies (KETs) such as Internet of Things (IoT), tools for big data management and processing, Cloud/Edge/Fog computing, Artificial Intelligence (AI), Blockchain reached an advanced maturity, and therefore the potential for revolutionizing the whole tele-healthcare sector.

Multi-objective algorithms based on nature-inspired approach of Extremal optimization (EO) used in distributed processor load balancing have been studied in the paper. EO defines task migration aiming at processor load balancing in execution of graph-represented distributed programs. In the multi-objective EO approach, three objectives relevant to distributed processor load balancing are simultaneously controlled: the function dealing with the computational load imbalance in execution of application tasks on processors, the function concerned with the communication between tasks placed on distinct computing nodes and the function related to the task migration number. An important aspect of the proposed multiobjective approach is the method for selecting the best solutions from the Pareto set. Pareto front analysis based on compromise solution approach, lexicographic approach and hybrid approach (lexicographic + numerical threshold) has been performed in dependence on the program graph features, the executive system characteristics and the experimental setting. The algorithms are assessed by simulation experiments with macro data flow graphs of programs run in distributed systems. The experiments have shown that the multi-objective EO approach included into the load balancing algorithms visibly improves the quality of program execution.

A physiological model improves delivered healthcare, when constructing a medical device. Such a model comprises a number of parameters. While an analytical method determines model parameters, an evolutionary algorithm can improve them further. As evolutionary algorithms were designed on top of random-number generators, their results are not deterministic. This raises a concern about their applicability to medical devices. Medical-device algorithm must produce an output with a minimum guaranteed accuracy. Therefore, we applied de-randomized sequences to Meta-Differential Evolution instead of using a random-number generator. Eventually, we designed an optimization method based on zooming with derandomized sequences as an alternative to the Meta-Differential Evolution. As the experimental setup, we predicted glucose-level signal to cover a blind window of glucose-monitoring signal that results from a physiological lag in glucose transportation. Completely de-randomized differential evolution exhibited the same accuracy and precision as completely non-deterministic differential evolution. They produced 93% of glucose levels with relative error less than or equal to 15%.

The PaGMO framework offers several optimization algorithms to determine optimal parameters of a black-box model. Such a model could be, for example, that for glucose homeostasis. As we are concerned about calculating and predicting glucose levels for diabetic patients, we evaluate the PaGMO framework for this particular task. Using three scenarios, we test PaGMO's individual algorithms and compare them to our previous results, which we obtained with de-randomized Meta-Differential Evolutions. All testing scenarios address real aspects of processing a signal of the continuous glucose monitoring system. Specifically, we address signal reconstruction and prediction.

Health 4.0 can provide effective ways to improve the health status of subjects by taking advantage of cyber-physical systems and Internet of things technologies for the solution of healthcare problems. One of these is represented by suitably estimating blood pressure values of subjects in a continuous, real-time, and noninvasive way. To address it, we propose an approach only requiring a photoplethysmography (PPG) sensor and a mobile/desktop device. The approach avails itself of genetic programming to automatically find an explicit relationship between blood pressure values and PPG ones. This relationship is tested on a set of 11 subjects and compared against other regression methods, and turns out to be better. Namely, the root-mean-square error values are equal to 8.49 and 6.66 for the systolic and the diastolic blood-pressure values, respectively. Those for the relative error, instead, are equal to 5.55% for the systolic and 6.59% for the diastolic values.

The wide availability of sensing devices in the medical domain causes the creation of large and very large data sets. Hence, tasks as the classification in such data sets becomes more and more difficult. Deep Neural Networks (DNNs) are very effective in classification, yet finding the best values for their hyper-parameters is a difficult and time-consuming task. This paper introduces an approach to decrease execution times to automatically find good hyper-parameter values for DNN through Evolutionary Algorithms when classification task is faced. This decrease is obtained through the combination of two mechanisms. The former is constituted by a distributed version for a Differential Evolution algorithm. The latter is based on a procedure aimed at reducing the size of the training set and relying on a decomposition into cubes of the space of the data set attributes. Experiments are carried out on a medical data set about Obstructive Sleep Anpnea. They show that sub-optimal DNN hyper-parameter values are obtained in a much lower time with respect to the case where this reduction is not effected, and that this does not come to the detriment of the accuracy in the classification over the test set items.

The patients suffering from diabetes are subjected to several serious medical risks that can lead also to fatal consequences. To enhance the quality of life of these patients there is the necessity to devise an artificial pancreas able to inject an insulin bolus when needed. This paper presents a genetic-programming based algorithm to extrapolate a regression model able to estimate the blood glucose (BG) level through interstitial glucose (IG) measurements and their derivatives. This algorithm represents a possible step in building the fundamental element of such an artificial pancreas, namely a new evolutionary computation-based metodology to derive a mathematical relationship between BG and IG. The proposed evolutionary automatic procedure is evaluated on a real-world database made up of both BG and IG measurements of people suffering from Type 1 diabetes. The discovered model is validated through a comparison with other techniques during the experimental phase.

Subjects suffering from Type 1 diabetes mellitus need to constantly receive insulin injections. To improve their life quality, a desirable solution is represented by the implementation of an artificial pancreas. In this paper we move a preliminary step towards this goal. Namely, we work at the knowledge base for such a device. One of the main problems is to estimate the Blood Glucose (BG) values, starting from the easily available Interstitial Glucose (IG) ones, and this is the aim of our paper. To face this regression task we avail ourselves of Genetic Programming over a real-world database containing both BG and IG measurements for several subjects suffering from Type 1 diabetes, aiming at finding an explicit relationship between BG and IG values under the form of a mathematical expression. This latter could be the core of the knowledge base part of an artificial pancreas. Experimental comparisons against the state-of-the-art models evidence the quality of the proposed approach.

In this paper a general-purpose asynchronous adaptive multi-population model for distributed Differential Evolution (AsAMP-dDE) algorithm is proposed. The distributed algorithm, following the stepping-stone model, is characterized by an asynchronous mechanism for the migration and for a multipopulation recombination employed to exchange information. The adaptive procedure is based on two steps. Firstly a local performance measure related to the average fitness improvement for each subpopulation is computed. Secondly, a specific updating scheme based on these measures takes place to randomly update the control parameter values. The asynchronous migration mechanism and the adaptive procedure allow reducing the number of control parameters to be set in the distributed model. AsAMP-dDE has been tested on the benchmarks of the CEC2016 real parameter single objective competition without adopting any specific mechanism opportunely tailored for solving such test problems. The results show that this algorithm allows obtaining good performance in most of the investigated benchmarks.

The aim of this study is to identify an explicit relationship between life-style and the risk of falling under the form of a mathematical model. Starting from some personal and behavioral information as, e.g., weight, height, age, data about physical activity habits, and concern about falling, the model would easily estimate the score of the Mini-Balance Evaluation Systems (Mini-BES) test. This would make fall risk assessment less invasive, because subjects would not need to undergo the classical Mini-BES test, rather they could estimate it at home by answering some questionnaires. The mathematical model obtained in this study has been tested over a subset of unseen subjects and the results show an average error of ±2.74.

The problem of automatically extracting novel and interesting knowledge from large amount of data is often performed heuristically when pattern extraction through classical statistical methods is found hard. In this paper an evolutionary approach, based on Differential Evolution, is proposed, which is able to perform the automatic discovery of comprehensible classification rules as a set of IF...THEN rules over a database of Multiple Sclerosis potential lesions. Moreover, this tool also determines which the most discriminant database attributes are in categorizing instances. Therefore, this evolutionary tool provides an efficient decision support system for clinical decisions, that could be a useful tool for medical experts to help them gain insight into the reasons for assessing the abnormality of a lesion.

Continuous blood pressure (BP) measurement is an important issue in the medical field. The hypothesis of existence of a nonlinear relationship between plethysmography (PPG) and BP values has been investigated in this paper. If this hypothesis is true, then it is possible to indirectly measure patient's BP in a non-invasive way through the application of a wearable wireless PPG sensor to patient's finger and through the use of the results of a regression analysis aimed at linking PPG and BP values. To find the relationship between these two biomedical characteristics we have used here Genetic Programming (GP), because in a regression task it can evolve in an automatic way the structure of the most suitable explicit mathematical model. An analysis of the related scientific literature shows that this is the first attempt to mathematically relate PPG and BP values through GP. In this paper some preliminary experiments on the use of GP in facing this regression task have been carried out. As a result, for both systolic and diastolic BP values explicit mathematical models providing nonlinear relationship between PPG and BP values have been achieved, involving an approximation error of around 2 mmHg in both cases. A prototypal mobile-based system has been realized which is able to continuously estimate in real time the two BP values for any given patient by using only a plethysmography signal and the obtained mathematical models.

This paper presents an adaptive model for automatically pair-wise registering range images. Given two images and set one as the model, the aim is to find the best possible spatial transformation of the second image causing 3D reconstruction of the original object. Registration is effected here by using a distributed Differential Evolution algorithm characterized by a migration model inspired by the phenomenon known as biological invasion, and by applying a parallel Grid Closest Point algorithm. The distributed algorithm is endowed with two adaptive updating schemes to set the mutation and the crossover parameters, whereas the subpopulation size is assumed to be set in advance and kept fixed throughout the evolution process. The adaptive procedure is tied to the migration and is guided by a performance measure between two consecutive migrations. Experimental results achieved by our approach show the capability of this adaptive method of picking up efficient transformations of images and are compared with those of a recently proposed evolutionary algorithm. This efficiency is evaluated in terms of both quality and robustness of the reconstructed 3D image, and of computational cost.

The paper concerns methods for using ExtremalOptimization (EO) for processor load balancing during executionof distributed programs. A load balancing algorithm for clustersof multicore processors is presented and discussed. In thisalgorithm the EO approach is used to periodically detect thebest tasks as candidates for migration and for a guided selectionof the best processors to receive the migrated tasks. To decreasethe complexity of selection for migration, we propose a guidedEO algorithm which assumes a two step stochastic selectionduring the solution improvement based on two separate fitnessfunctions. The functions are based on specific program modelswhich estimate relations between the programs and the executivehardware. The proposed load balancing algorithm is assessedby experiments with simulated load balancing of distributedprogram graphs. The algorithm is compared against an EO -- based algorithm with random placement of migrated tasks anda classic genetic algorithm.

Detection and real time monitoring of obstructive sleep apnea (OSA) episodes are very important tasks in healthcare. To suitably face them, this paper proposes an easy-to-use, cheap mobile-based approach relying on three steps. First, single-channel ECG data from a patient are collected by a wearable sensor and are recorded on a mobile device. Second, the automatic extraction of knowledge about that patient takes place offline, and a set of IF…THEN rules containing heart-rate variability (HRV) parameters is achieved. Third, these rules are used in our real-time mobile monitoring system: the same wearable sensor collects the single-channel ECG data and sends them to the same mobile device, which now processes those data online to compute HRV-related parameter values. If these values activate one of the rules found for that patient, an alarm is immediately produced. This approach has been tested on a literature database with 35 OSA patients. A comparison against five well-known classifiers has been carried out.

Automatic fall detection is a major issue in taking care of the health of elderly people. In this task the capability of telling in real time falls from normal daily activities is crucial. To this aim, this paper proposes an approach based on the automatic extraction of knowledge expressed as a set of IF...THEN rules from a database of fall recordings. This set of rules, generated offline, can then be exploited in a real-time mobile monitoring system: data gathered by wearable sensors are processed in real time and, if their values activate some of the rules describing falls, an alarm message is automatically produced. The approach has been compared against other classifiers on a real-world fall database, and its discrimination ability is shown to be higher. Moreover, a test phase for the real-time mobile monitoring system is being carried out over real cases.

Performing detection and real-time monitoring of Obstructive Sleep Apnea (OSA) is a significant healthcare task. An easy, cheap, and mobile approach to monitor patients with OSA is proposed here. It gathers data from a patient by a single-channel ECG, and offline automatically extracts knowledge about that patient as a set of IF...THEN rules containing Heart Rate Variability (HRV) parameters. These rules are then used in the real-time mobile monitoring system: ECG data is collected by a wearable sensor, sent to a mobile device, and processed online to compute HRV-related parameter values. If a rule is activated by those values, the system produces an alarm. A literature database of OSA patients has been used to test the approach.

In this paper, a new approach based on Differential Evolution for the automatic classification of items in medical databases is proposed. Based on it, a tool called DEREx is presented, which automatically extracts explicit knowledge from the database under the form of IF-THEN rules. DEREx is thought as a useful support to decision making whenever explanations on why an item is assigned to a given class should be provided, as it is the case for diagnosis in the medical domain. The tool has been compared over seven medical databases against a set of fifteen classification tools widely used in literature. The results have proven the effectiveness of the proposed approach, since DEREx turns out to be among the very best tools in terms of highest classification accuracy, so it is preferable because it automatically extracts knowledge and provides users with it under an easily comprehensible form.

Abstract In this paper attention is concentrated on the mapping of computationally intensive multi‐task applications onto shared computational grids. This problem, already known to be as NP‐complete in parallel systems, becomes even more arduous in such environments. To find a near‐optimal mapping solution a parallel version of a Differential Evolution algorithm is presented and evaluated on different applications and operating conditions of the grid nodes. The purpose is to select for a given application the mapping solutions that minimize the greatest among the time intervals which each node dedicates to the execution of the tasks assigned to it. The experiments, effected with applications represented as task interaction graphs, demonstrate the ability of the evolutionary tool to perform multisite grid mapping, and show that the parallel approach is more effective than the sequential version both in enhancing the quality of the solution and in the time needed to get it. Copyright © 2011 John Wiley & Sons, Ltd.

Target behaviours can be achieved by finding suitable parameters for Continuous Time Recurrent Neural Networks (CTRNNs) used as agent control systems. Differential Evolution (DE) has been deployed to search parameter space of CTRNNs and overcome granularity, boundedness and blocking limitations. In this paper we provide initial support for DE in the context of two sample learning problems.

This paper deals with the design and implementation of a parallel software system based on differential evolution for the registration of images, and with its testing on two bidimensional remotely sensed images on mosaicking problem. Registration is carried out by finding the most suitable affine transformation in terms of maximization of the mutual information between the first image and the transformation of the second one, without any need for setting control points. A coarse-grained distributed version is implemented on a cluster of personal computers

Increase in intensive applications with different computational requirements, coupled with the unification of remote and diverse resources thanks to advances in the wide-area network technologies and the low cost of components, have encouraged the development of grid computing. To exploit the promising potentials of geographically distributed resources, effective and efficient mapping algorithms are fundamental. Since the problem of optimally mapping is NP-complete, the development of evolutionary techniques to find near-optimal solutions is welcome. In this paper a distributed system based on differential evolution is designed and implemented to face the mapping problem in a grid environment aiming at reducing the degree of use of the grid resources. This system is tested on some different resource allocation scenarios

An innovative approach to spectral pattern recognition for multispectral images based on genetic programming is introduced. The problem is faced in terms of unsupervised pixel classification. The system is tested on a multispectral image with 31 spectral bands and 256-256 pixels. A good quality clustered output image is obtained.

The search for novel and useful patterns within large databases, known as data mining, has become of great importance owing to the ever-increasing amounts of data collected by large organizations. In particular, the emphasis is on heuristic search methods which are able to discover patterns that are hard or impossible to detect using standard query mechanisms and classical statistical techniques. In this paper, an evolutionary system that is capable of extracting explicit classification rules is presented. The results are compared with those obtained by other approaches.