To support the goal of sustainable manufacturing, recent studies have emphasized energy-efficient production scheduling, with the energy-oriented hybrid flow shop scheduling problem with limited buffers (EO-HFSP-LB) being particularly relevant in energy-intensive industries such as steel, cement, and aluminum. In this paper, we investigate an EO-HFSP-LB that simultaneously minimizes total weighted tardiness (TWT) and non-processing energy (NPE), two conflicting and non-regular objectives. To address this problem, we propose a learning-guided multi-objective evolutionary algorithm (LgMOEA) that can efficiently search for Pareto-optimal solutions by leveraging problem-specific knowledge. The main components of LgMOEA include: (1) a forward–backward scheduling procedure for solution decoding; (2) objective-guided genetic and neighborhood operators to effectively explore and exploit the solution space; and (3) a learning-guided operator selection module that dynamically balances exploration and exploitation. The proposed algorithm is evaluated on 25 well-synthesized benchmark instances. Computational results show that: (1) the LgMOEA outperforms state-of-the-art multi-objective algorithms in terms of hypervolume, spacing, and success rate; and (2) each module positively contributes to the search for Pareto-optimal solutions. The source code of LgMOEA is made available at: https://github.com/janason/Soft-Scheduling/tree/master/LgMOEA .

Process industries typically exhibit highly coupled process variables and fluctuations in raw materials, making accurate quality prediction challenging. Conventional methods rely on either parameter-quality relevant modeling or temporal modeling. However, these methods often overlook the dynamic coupling effects, thereby limiting their predictive capabilities. To address this, we present an integrated framework that combines graph-based computation with gated recurrent units (GRUs) to explicitly model dynamic coupling in quality prediction. In the proposed framework, process parameters and quality indicators are represented as graph nodes, while interactions among variables are encoded as edges. Production data are used to initialize the node features, and graph neural networks are used to extract relevant representations. These representations are then passed to a graph-informed GRU (GIGRU) cell, an enhanced GRU architecture that incorporates internal mechanisms for feature selection and state updating. By integrating coupling information with memory and coupling update gates, the architecture enables joint modeling of relevant and temporal dependencies, providing stronger representational power than traditional methods. The proposed approach was evaluated on data from a tobacco production line. Experimental results showed that our GIGRU model achieved a mean absolute error of 0.061, a mean squared error of 0.605, and a coefficient of determination of 0.942, outperforming the other prediction methods compared. Ablation studies further confirm the model’s improved ability to capture both temporal dynamics and relational coupling in process data. Overall, the proposed approach enhances prediction accuracy and robustness, offering more reliable support for quality monitoring and optimization in process industries.

Predicting and extending the remaining life of cutting tools during machining processes is essential for sustainable manufacturing. Traditional prognosis methods often struggle to adapt to different working conditions over the machining process lifecycle. This paper proposes a novel framework that effectively addresses the challenges by integrating multi-source data and using deep learning techniques. The system integrates augmented-power and vibration data collected from computer numerical control machines with the following innovations: (1) A hybrid temporal convolutional network (TCN)-attention model is developed for cutting tool remaining life prognosis, which achieves the best accuracy of 98.51 % and average of 97.62 %. In addition, optimal laser shock peening parameters are selected using a deep neural network and enhanced ternary bees algorithm. (2) A time-series generative adversarial network is used for data augmentation, which increases data quantity for TCN model training. (3) Data quality is evaluated using the t-distributed stochastic neighbor embedding, Fréchet inception distance, and root mean squared error to ensure similarity between real and generated data. (4) The effectiveness of the remanufacturing approach is validated with a 28.95 % and 30.77 % increase in tool life based on finite element analysis and experimental testing, respectively. This comprehensive approach contributes to enhancing tool life prediction accuracy and optimizing sustainable remanufacturing processes, thereby enhancing production efficiency and reducing waste in machining operations.

Many scenarios in the process manufacturing industry, such as production in steel plants, can be modeled through hybrid flow shop scheduling problems. Continuous processing is a crucial constraint in this type of production, playing a vital role in maintaining the continuity of operations. Meanwhile, limited processing resources create threshold constraints that affect the conditions for starting production and lead to new bottlenecks. Therefore, we introduce and study the hybrid flow shop scheduling problem with continuous processing and resource threshold constraints (HFSSP-CPRT). To solve this problem, a population-based iterative greedy algorithm with bidirectional decoding (PBIGA-BD) is proposed. To improve encoding accuracy and decoding efficiency, stages with continuous processing and resource threshold constraints are encoded separately, and bidirectional decoding is applied. Subsequently, six initialization solutions are designed to improve the quality of the initial population. To enhance the diversity of the superior population, an improved destruction-construction method is designed. Then, a hyper-heuristic local search is developed to reduce the blindness of local search. It consists of six neighborhood search actions by considering the characteristics of HFSSP-CPRT. We tested the effectiveness of PBIGA-BD by applying it to an HFSSP-CPRT case of steel plant in China. Comprehensive experimental results showed that the proposed PBIGA-BD is able to outperform several heuristic rules and state-of-the-art algorithms being compared.

In many organizations, retrieving valuable information from complex databases has traditionally required specialized technical skills, often leaving non-technical professionals dependent on others for timely insights. This study presents an approach that allows anyone, even without knowledge of query languages, to directly interact with databases by asking questions in everyday language. We achieve this by combining advanced generative language models, such as a high-capacity Generative Pre-trained Transformer (GPT) model, with intelligent software agents that translate natural language queries into precise SQL statements. Our evaluation compares different strategies, including models specifically trained on a particular database domain versus those guided by only a handful of examples. The results show that training a model with tailored examples yields more accurate and reliable database queries than relying solely on minimal guidance for the given use case. This work highlights the practical value of refining model complexity and balancing computational costs to empower business users with easy, direct access to data. By reducing reliance on technical teams, organizations can enable faster, more informed decision-making and foster a more inclusive environment where everyone can uncover data-driven insights on their own.

Artificial intelligence-based machine learning models have been widely used to explore and address various mental health-related problems in recent years, including depression. In this study, we present an ensemble approach to complement the 90 unique input features that we proposed in a previous study on depression detection using social media texts. Our proposed Ensemble of Ensemble Classifiers (EECs) combines many ensemble models, including Bagging Predictors, Random Forests, Adaptive Boosting and Gradient Boosting, as inner ensembles. These inner ensembles are arranged in a parallel fashion, where each of them is trained using different subsets of data sampled from the training data via bootstrap sampling. After the models are trained, during the testing phase, the results of all inner ensembles are processed using two methods— majority vote or class priority threshold—to get the final result as an output. From the experiments, we find that EECs are accurate in detecting signs of depression in social media users by analysing their posts in social media platforms such as Twitter. Our approach outperforms other ensemble methods on the public datasets we used. Moreover, if set correctly, the parameters of EECs can further improve the performance of the proposed ensemble in detecting signs of depression.

Financial news disclosures provide valuable information for traders and investors while making stock market investment decisions. Essential but challenging, the stock market prediction problem has attracted significant attention from both researchers and practitioners. Conventional machine learning models often fail to interpret the content of financial news due to the complexity and ambiguity of natural language used in the news. Inspired by the success of recurrent neural networks (RNNs) in sequential data processing, we propose an ensemble RNN approach (long short-term memory, gated recurrent unit, and SimpleRNN) to predict stock market movements. To avoid extracting tens of thousands of features using traditional natural language processing methods, we apply sentiment analysis and the sliding window method to extract only the most representative features. Our experimental results confirm the effectiveness of these two methods for feature extraction and show that the proposed ensemble approach is able to outperform other models under comparison.

To address the problem of miss- and false detection during quality inspection of lithium-ion battery cover screen printing (LBCSP), we propose a hybrid image registration method using a point-based feature extraction algorithm and nonlinear-scale space construction. Our proposed method integrates the accelerated-KAZE algorithm with the boosted efficient binary local image descriptor (BEBLID), and is named A-BEBLID. Facing the challenge of the inevitable offset caused by machine vibration during production, we combine a nonlinear diffusion filter with a local image descriptor to extract features from images, and then use the grid-based motion statistics algorithm to remove the incorrect matching pairs. We tested the method on a custom dataset created using images taken from actual lithium-ion battery production lines, named LBCSP. We also evaluated the method on the public HPatches dataset. The average precision achieved by A-BEBLID on the LBCSP dataset is 89% (threshold: 2 pixels), with a localization error of 1.11 pixels, while on the HPatches dataset, the average precision is 73% (threshold: 2 pixels), with a localization error of 1.52 pixels. Comprehensive experimental results also showed that the proposed A-BEBLID can outperform other approaches found in the literature. The method can be further applied to other industry scenarios with similar image registration requirements.

The financial impact of online reviews has prompted some fraudulent sellers to generate fake consumer reviews for either promoting their products or discrediting competing products. In this study, we propose a novel ensemble model—the Multi-type Classifier Ensemble (MtCE) —combined with a textual-based featuring method, which is relatively independent of the system, to detect fake online consumer reviews. Unlike other ensemble models that utilise only the same type of single classifier, our proposed ensemble utilises several customised machine learning classifiers (including deep learning models) as its base classifiers. The results of our experiments show that the MtCE can adequately detect fake reviews, and that it outperforms other single and ensemble methods in terms of accuracy and other measurements for all the relevant public datasets used in this study. Moreover, if set correctly, the parameters of MtCE, such as base-classifier types, the total number of base classifiers, bootstrap, and the method to vote on output (e.g., majority or priority), can further improve the performance of the proposed ensemble.

In recent years, deep Convolutional Neural Networks (CNNs) have shown great potential in malware classification. CNNs, which are originally designed for image processing, identify malware binaries visualised as images. Despite offering promising performance, these human-designed networks are very large requiring more resources to train and deploy them. Evolutionary algorithms have been successfully used in designing deep neural networks automatically for different application domains. In this work, we use a Genetic Algorithm (GA) to optimise the CNN topology and hyperparameters for image-based malware classification. Computational experiments with two different malware datasets, Malimg and Microsoft Malware, show that the GA-evolved networks are very competitive to the networks designed by experts in classifying malware, yet they are also considerably smaller in size comparison.

Energy-efficient production scheduling research has received much attention because of the massive energy consumption of the manufacturing process. In this article, we study an energy-efficient job-shop scheduling problem with sequence-dependent setup time, aiming to minimize the makespan, total tardiness and total energy consumption simultaneously. To effectively evaluate and select solutions for a multiobjective optimization problem of this nature, a novel fitness evaluation mechanism (FEM) based on fuzzy relative entropy (FRE) is developed. FRE coefficients are calculated and used to evaluate the solutions. A multiobjective optimization framework is proposed based on the FEM and an adaptive local search strategy. A hybrid multiobjective genetic algorithm is then incorporated into the proposed framework to solve the problem at hand. Extensive experiments carried out confirm that our algorithm outperforms five other well-known multiobjective algorithms in solving the problem.

While previous research established that culture plays an important role in technology adoption, there is only limited work on the role of cultural appropriateness in user interface design for users from a specific background. In this study, we focus on the case of avatar design as a user interface element for facilitating positive user experience. Building on the theoretical lenses of social response theory and the “Computers Are Social Actors” paradigm, we develop a research model to investigate how cultural appropriateness of avatar design is a vital driver for users’ trust. We evaluate our research model by means of an online experiment ( n = 313) in the context of online health advice for users from Saudi Arabia. The avatars differed in appearance (Arab, non-Arab), gender (male, female), and clothing (athletic, medical, everyday). Our results show that Arab avatars exhibited significantly higher cultural appropriateness than non-Arab avatars. Furthermore, participants were more inclined to select an Arab avatar (88.2%) that matched their gender (77.3%). Confirming the critical role of cultural appropriateness, our study demonstrates the importance of carefully considering the target audience in designing user interfaces.

This article presents a new model to handle the cast break problem caused by small daily disruptions in the processing time of the steelmaking and continuous casting (SCC) production process. In this model, the exact distribution of the uncertain parameters is unknown, and support set, mean, and covariance information is used to describe the uncertain processing time. The problem aims to determine the assignments, sequences, and time points of the charges to be processed on corresponding machines. The main goal is to minimize the expected value of the production objective while reducing the number of cast break occurrences. The problem is solved in two steps. First, a subproblem is developed by fixing the sequences and the assignments of the charges. This subproblem is formulated as a distributionally robust chance-constrained (DRCC) model, in which the constraints are established with certain probabilities even when the uncertain processing times are in their worst cases. A dual approximation method is proposed to convert the model into a semidefinite programming problem so that it can be solved by standard solvers. Additionally, a linear programming approximation method is used to accelerate the solving procedure. A Tabu search algorithm incorporated with a speed-up strategy is also designed to determine the assignments and sequences of the charges. Both simulated data generated from different distributions and actual production data are used to test the efficacy of our model. Results of the numerical experiments show that the schedule obtained from the DRCC model is more robust, i.e., it causes fewer cast breaks than the nominal schedule obtained from a deterministic model.

Automatic human affect recognition is a key step towards more natural human-computer interaction. Recent trends include recognition in the wild using a fusion of audiovisual and physiological sensors, a challenging setting for conventional machine learning algorithms. Since 2010, novel deep learning algorithms have been applied increasingly in this field. In this paper, we review the literature on human affect recognition between 2010 and 2017, with a special focus on approaches using deep neural networks. By classifying a total of 950 studies according to their usage of shallow or deep architectures, we are able to show a trend towards deep learning. Reviewing a subset of 233 studies that employ deep neural networks, we comprehensively quantify their applications in this field. We find that deep learning is used for learning of (i) spatial feature representations, (ii) temporal feature representations, and (iii) joint feature representations for multimodal sensor data. Exemplary state-of-the-art architectures illustrate the progress. Our findings show the role deep architectures will play in human affect recognition, and can serve as a reference point for researchers working on related applications.

While the elderly population is growing rapidly, acceptance and use of m-government services by them are far below expectation. Previous studies on acceptance and use of m-government services have predominantly focused on younger citizens with skills and experience of information technologies. Drawing upon the dual factor model, this study investigates the enablers and inhibitors of the elderly's m-government service adoption behavior. Four constructs from the unified theory of acceptance and use of technology (UTAUT), namely, performance expectancy, effort expectancy, facilitating conditions, social influence; and self-actualization are treated as enablers, while user resistance to change, technology anxiety, and declining physiological conditions are regarded as inhibitors. Results show that adoption of m-government by the elderly is significantly influenced by all tested enablers and inhibitors, except for social influence. This study contributes by providing an integrative model of technology acceptance for the elderly along with practical implications for policy makers.

Dial-a-ride (DAR) systems are popular nowadays in transportation services because of their affordable price and convenience. The increasing demand for DAR service has an impact on greenhouse gas emissions, but limited past studies in the relevant literature have considered this. In this paper, we present a green heterogeneous DAR problem inspired by Australian DAR service of elderly, patients and disabled individuals. The problem aims to route a fleet of heterogeneous vehicles to transport a set of users with different requirements, which include minimising the total routing cost and total CO2emission simultaneously. To solve the problem, a Regional Multi-Objective Tabu Search (RMOTS) algorithm is proposed, taking the decision maker's preferences of the objectives into account, and consequently concentrating on a specific area of the Pareto front. To evaluate the performance of RMOTS, it is compared with two algorithms from the literature developed for similar problems. Experimental results show that the proposed RMOTS is able to outperform these algorithms based on the performance measures considered.

Migration is one of the many responses humans and societies make to ongoing demographic, economic, societal and environmental changes. In this work, we use agent-based modeling (ABM) to study the dynamics of migration flows across provinces and cities in the Mekong Delta, Vietnam. The strength of ABM is that it allows a bottom-up approach that focuses on how individuals make decisions in a complex system comprising various factors. Outputs of our agent-based model are automatically calibrated with actual data using a genetic algorithm. This automated calibration yields some significant improvement in the results, with all observed net- and out-migration data captured within the 95% confidence interval. Sensitivity analysis carried out helps to further understand the impact of critical factors on the final migration decision.

Two factors that have a major impact on the performance of an optimization method are (1) formal algorithm specifications and (2) practical implementations. The impact of the latter is typically ignored, although it defines the results measured in experiments. We present an in-depth study of algorithm implementation issues and ask questions such as Does optimizing the implementation of an optimization algorithm pay off? Do bugs matter? and Is using more complicated but also more efficient data structures worth the effort? The intuitive answer to all of these questions is yes, but there is little published evidence. To bridge this gap, we use one of the most studied combinatorial optimization problems – the Traveling Salesman Problem – as a test bed and implement two state-of-the-art approaches for solving it – the Lin-Kernighan Heuristic and an Ejection Chain Method. We investigate implementation effort and performance gain, in order to provide further insights to the above questions.

Trust and trustworthiness are of great importance in social and human systems, especially when considering managerial and economic decision-making. In this paper, we investigate the emergent dynamics of an evolutionary game-theoretic model-the N-player evolutionary trust game-consisting of three types of players: 1) an investor; 2) a trustee who is trustworthy; and 3) a trustee who is untrustworthy. Here, we limit the interactions between players to local neighborhoods defined by a specific spatial topology or social network. Players are able to adjust their game-playing strategies using an evolutionary update rule based on the payoffs obtained by their neighbors. Through comprehensive simulation experiments, we find that trust can be promoted when players interact on a social network despite a substantial number of untrustworthy individuals in the initial population. These results differ from findings reported for an unstructured population of the same game, where the existence of a single untrustworthy individual would eliminate trust completely. We compare the dynamics of the model with different social network densities and structures (e.g., from regular lattices to scale-free and random networks). We observe that the levels of trust vary under different network structures, and the level is correlated with how “difficult” the game is. When game conditions are easy (i.e., low temptation to defect and/or almost no initial untrustworthy trustees), homogeneous networks with higher densities can promote higher levels of trust. However, when the game becomes harder, heterogeneous social networks with lower densities are able to promote higher levels of trust and global net wealth.

In this paper, we present an evolutionary trust game to investigate the formation of trust in the so-called sharing economy from a population perspective. To the best of our knowledge, this is the first attempt to model trust in the sharing economy using the evolutionary game theory framework. Our sharing economy trust model consists of four types of players: a trustworthy provider, an untrustworthy provider, a trustworthy consumer, and an untrustworthy consumer. Through systematic simulation experiments, five different scenarios with varying proportions and types of providers and consumers were considered. Our results show that each type of players influences the existence and survival of other types of players, and untrustworthy players do not necessarily dominate the population even when the temptation to defect (i.e., to be untrustworthy) is high. Our findings may have important implications for understanding the emergence of trust in the context of sharing economy transactions.

Malicious web domains represent a big threat to web users' privacy and security. With so much freely available data on the Internet about web domains' popularity and performance, this study investigated the performance of well-known machine learning techniques used in conjunction with this type of online data to identify malicious web domains. Two datasets consisting of malware and phishing domains were collected to build and evaluate the machine learning classifiers. Five single classifiers and four ensemble classifiers were applied to distinguish malicious domains from benign ones. In addition, a binary particle swarm optimisation (BPSO) based feature selection method was used to improve the performance of single classifiers. Experimental results show that, based on the web domains' popularity and performance data features, the examined machine learning techniques can accurately identify malicious domains in different ways. Furthermore, the BPSO-based feature selection procedure is shown to be an effective way to improve the performance of classifiers.

The industrial sector is one of the largest energy consumers in the world. To alleviate the grid's burden during peak hours, time-of-use (TOU) electricity pricing has been implemented in many countries around the globe to encourage manufacturers to shift their electricity usage from peak periods to off-peak periods. In this paper, we study the unrelated parallel machine scheduling problem under a TOU pricing scheme. The objective is to minimize the total electricity cost by appropriately scheduling the jobs such that the overall completion time does not exceed a predetermined production deadline. To solve this problem, two solution approaches are presented. The first approach models the problem with a new time-interval-based mixed integer linear programming formulation. In the second approach, we reformulate the problem using Dantzig-Wolfe decomposition and propose a column generation heuristic to solve it. Computational experiments are conducted under different TOU settings and the results confirm the effectiveness of the proposed methods. Based on the numerical results, we provide some practical suggestions for decision makers to help them in achieving a good balance between the productivity objective and the energy cost objective.

This paper presents a novel generalised indirect reciprocity approach for promoting cooperation in social dilemma games. Here, players decide upon an action to play in the game based on public information (or “external cues”) rather than individual-specific information. The public information is constantly updated according to the underlying learning model. Comprehensive simulation experiments using the N-player Prisoner's Dilemma (PD) and Snowdrift (SD) games show that generalised indirect reciprocity promotes high levels of cooperation across a wide range of conditions. This is despite the fact that the make-up of player groups is continually changing. As expected, the extent of cooperative behaviour observed in the “constraint-relaxed” N-player SD game is significantly higher than the N-player PD game. Our proposed generalised indirect reciprocity model may shed light on the conundrum of cooperation between anonymous individuals.

Submarine search-evasion path planning aims to acquire an evading route for a submarine so as to avoid the detection of hostile anti-submarine searchers such as helicopters, aircraft and surface ships. In this paper, we propose a numerical optimization model of search-evasion path planning for invading submarines. We use the Artificial Bee Colony (ABC) algorithm, which has been confirmed to be competitive compared to many other nature-inspired algorithms, to solve this numerical optimization problem. In this work, several search-evasion cases in the two-dimensional plane have been carefully studied, in which the anti-submarine vehicles are equipped with sensors with circular footprints that allow them to detect invading submarines within certain radii. An invading submarine is assumed to be able to acquire the real-time locations of all the anti-submarine searchers in the combat field. Our simulation results show the efficacy of our proposed dynamic route optimization model for the submarine search-evasion path planning mission.

XCS - the extended Classifier System - combines an evolutionary algorithm with reinforcement learning to evolve a population of condition-action rules (classifiers). Typically, population-based approaches are slow and increasing the problem size (in terms of the number of features/samples) poses a real threat to the suitability of XCS for real-world applications. Thus, reducing the execution time without losing accuracy is highly desirable. Profiling of the execution of off-the-shelf XCS implementations suggests that the rule matching process is the most computational demanding step. A solution to this is parallelization, i.e., using parallel processing techniques to speed up the matching process (and thus the entire XCS learning process). There are many ways to achieve that, using Graphic Processing Units (GPUs) is one option. Originally, GPUs were designed to conduct a sequence of graphics operations in a massively parallel fashion. Today, GPUs can be used for all sorts of general purpose calculations that are normally handled by the CPU. In this paper, we propose a hybrid rule matching process using both CPU and GPU simultaneously for a maximum performance gain. Our experimental results indicate that this approach does speed up the XCS learning process, and that the GPU is the dominant powerful computing resource in the model.

We study the evolution of cooperation in a population of agents playing the N-player Prisoner's Dilemma game via stigmergic interactions. Here, agent decision making is guided by a shared pheromone table. Actions are played at each time step and a trace (or signal) corresponding to the rewards received is recorded in this shared table. Subsequent actions are then determined probabilistically using the shared information. Comprehensive Monte Carlo simulation experiments show that the stigmergy-based mechanism is able to promote cooperation despite the fact that the make-up of the interacting groups is continually changing. A direct comparison with a genetic algorithm-based N-player model confirms that the extent of cooperative behaviour achieved is significantly higher across a wide range of cost-to-benefit ratios. In the concluding remarks, we highlight the real-world implications of stigmergic interactions.

Mechanisms promoting the evolution of cooperation in two players and two strategies (22) evolutionary games have been investigated in great detail over the past decades. Understanding the effects of repeated interactions in multiplayer spatial games, however, is a formidable challenge. In this paper, we present a multiplayer evolutionary game model in which agents play iterative games in spatial populations. -player versions of the well-known Prisoner's Dilemma and the Snowdrift games are used as the basis of the investigation. These games were chosen as they have emerged as the most promising mathematical metaphors for studying cooperative phenomena. Here, we have adopted an experimental approach to study the emergent behavior, exploring different parameter configurations via numerical simulations. Key model parameters include the cost-to-benefit ratio, the size of groups, the number of repeated encounters, and the interaction topology. Our simulation results reveal that, while the introduction of iterated interactions does promote higher levels of cooperative behavior across a wide range of parameter settings, the cost-to-benefit ratio and group size are important factors in determining the appropriate length of beneficial repeated interactions. In particular, increasing the number of iterated interactions may have a detrimental effect when the cost-to-benefit ratio and group size are small.

The evolution of strategies in iterated multi-player social dilemma games is studied on small-world networks. Two different games with varying reward values - the N-player Iterated Prisoner's Dilemma (N-IPD) and the N-player Iterated Snowdrift game (N-ISD) - form the basis of this study. Here, the agents playing the game are mapped to the nodes of different network architectures, ranging from regular lattices to small-world networks and random graphs. In a given game instance, the focal agent participates in an iterative game with N-1 other agents drawn from its local neighbourhood. We use a genetic algorithm with synchronous updating to evolve agent strategies. Extensive Monte Carlo simulation experiments show that for smaller cost-to-benefit ratios, the extent of cooperation in both games decreases as the probability of re-wiring increases. For higher cost-to-benefit ratios, when the re-wiring probability is small we observe an increase in the level of cooperation in the N-IPD population, but not the N-ISD population. This suggests that the small-world network structure with small re-wiring probabilities can both promote and maintain higher levels of cooperation when the game becomes more challenging.

The problem of evolving and maintaining cooperation in both ecological and artificial multi-agent systems has intrigued scientists for decades. In this paper, we present an evolutionary game model that combines direct and spatial reciprocity to investigate the effectiveness of two different learning mechanisms used to promote cooperative behaviour in a social dilemma game - the N-player Iterated Prisoner's Dilemma (NIPD). Unlike the two-player game, in the NIPD the action of a player typically results in a non Pareto-optimal outcome for all other players within a social group given the relative costs and benefits associated with particular actions. Consequently, promoting system-wide cooperation is extremely difficult. We use comprehensive Monte Carlo simulation experiments to show that evolutionary-based strategy adaptation and update leads to significantly higher levels of cooperation in the NIPD when compared to social learning via cultural imitation. This finding suggests that when designing decentralised multi-agent systems, evolutionary adaptation mechanisms should be incorporated into the model where efficient collective actions are required.

The Cutting Stock Problem (CSP) is a combinatorial optimisation problem that involves cutting large stock sheets into smaller pieces. It has attracted vast attention along the years due to its applicability in many industries ranging from steel, glass, wood, plastic to paper manufacturing. A good solution to CSP is thus important as a mean to increase efficiency in these industrial sectors. In this paper, we present a selective mutation based evolutionary programming (SMBEP) for solving CSP without contiguity. We conduct experiments with our novel SMBEP on the benchmark problems of CSP. We show that the performance of our approach is slightly better than the previous results.