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He is author of the books "Artificial Neural Networks for Modelling and Control of Non-linear Systems" (Springer) and "Least Squares Support Vector Machines" (World Scientific), co-author of the book "Cellular Neural Networks, Multi-Scroll Chaos and Synchronization" (World Scientific) and editor of the books "Nonlinear Modeling: Advanced Black-Box Techniques" (Springer), "Advances in Learning Theory: Methods, Models and Applications" (IOS Press) and "Regularization, Optimization, Kernels, and Support Vector Machines" (Chapman & Hall/CRC). [ABSTRACT FROM AUTHOR]

Recently, evolutionary computation (EC) has experienced significant advancements due to the integration of machine learning, distributed computing, and big data technologies. These developments have led to new research avenues in EC, such as distributed EC and surrogate-assisted EC. While these advancements have greatly enhanced the performance and applicability of EC, they have also raised concerns regarding privacy leakages, specifically the disclosure of optimal results and surrogate models. Consequently, the combination of evolutionary computation and privacy protection becomes an increasing necessity. However, a comprehensive exploration of privacy concerns in evolutionary computation is currently lacking, particularly in terms of identifying the object, motivation, position, and method of privacy protection. To address this gap, this paper aims to discuss three typical optimization paradigms, namely, centralized optimization, distributed optimization, and data-driven optimization, to characterize optimization modes of evolutionary computation and proposes BOOM (i.e., oBject, mOtivation, pOsition, and Method) to sort out privacy concerns related to evolutionary computation. In particular, the centralized optimization paradigm allows clients to outsource optimization problems to a centralized server and obtain optimization solutions from the server. The distributed optimization paradigm exploits the storage and computational power of distributed devices to solve optimization problems. On the other hand, the data-driven optimization paradigm utilizes historical data to address optimization problems without explicit objective functions. Within each of these paradigms, BOOM is used to characterize the object and motivation of privacy protection. Furthermore, this paper discuss the potential privacy-preserving technologies that strike a balance between optimization performance and privacy guarantees. Finally, this paper outlines several new research directions for privacy-preserving evolutionary computation. [ABSTRACT FROM AUTHOR]

Large Language Models (LLMs) such as OpenAI's ChatGPT have achieved surprisingly huge progresses in the field of Natural Language Processing (NLP). This paper aims to present an immersive introduction to LLMs from the perspective of generative models. The main components of the training process of LLMs are explained, and an example of LLMs for AI-generated contents is given. This short paper is a summary of the interactive full paper online available at IEEE Xplore, in which detailed examples interactively demonstrate the training and working mechanisms of LLMs. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm called Forward Composition Propagation (FCP) to explain the predictions of feed-forward neural networks operating on structured classification problems. In the proposed FCP algorithm, each neuron is described by a composition vector indicating the role of each problem feature in that neuron. Composition vectors are initialized using a given input instance and subsequently propagated through the whole network until reaching the output layer. The sign of each composition value indicates whether the corresponding feature excites or inhibits the neuron, while the absolute value quantifies its impact. The FCP algorithm is executed on a post-hoc basis, i.e., once the learning process is completed. Aiming to illustrate the FCP algorithm, this paper develops a case study concerning bias detection in a fairness problem in which the ground truth is known. The simulation results show that the composition values closely align with the expected behavior of protected features. The source code and supplementary material for this paper are available at https://github.com/igraugar/fcp. [ABSTRACT FROM AUTHOR]

The 2024 IEEE Conference on Artificial Intelligence was held from 25-27 June at the Marina Bay Sands Conference Centre in Singapore (Figure 1). IEEE CAI 2024, the second edition of this conference and exhibition, emphasised AI applications and key AI verticals impacting industrial technology and innovation. Similar to the inaugural event in June 2023 at Santa Clara, California, the 2024 edition was co-sponsored by the IEEE Computational Intelligence Society (as the lead organisation for the first two editions), IEEE Computer Society, IEEE Signal Processing Society, and IEEE Systems, Man, and Cybernetics Society. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms, especially evolutionary algorithms, have been frequently used to find near optimal solutions to combinatorial optimization problems. The evaluation of such algorithms is often conducted through comparisons with other algorithms on a set of benchmark problems. However, even if one algorithm is the best among all those compared, it still has difficulties in determining the true quality of the solutions found because the true optima are unknown, especially in dynamic environments. It would be desirable to evaluate algorithms not only relatively through comparisons with others, but also in absolute terms by estimating their quality compared to the true global optima. Unfortunately, true global optima are normally unknown or hard to find since the problems being addressed are NP-hard. In this paper, instead of using true global optima, lower bounds are derived to carry out an objective evaluation of the solution quality. In particular, the first approach capable of deriving a lower bound for dynamic capacitated arc routing problem (DCARP) instances is proposed, and two optimization algorithms for DCARP are evaluated based on such a lower bound approach. An effective graph pruning strategy is introduced to reduce the time complexity of our proposed approach. Our experiments demonstrate that our approach provides tight lower bounds for small DCARP instances. Two optimization algorithms are evaluated on a set of DCARP instances through the derived lower bounds in our experimental studies, and the results reveal that the algorithms still have room for improvement for large complex instances. [ABSTRACT FROM AUTHOR]

Many real-world classification problems are becoming multi-label in nature, i.e., multiple class labels are assigned to an instance simultaneously. Multi-label classification is a challenging problem due to the involvement of three forms of interactions, i.e., feature-to-feature, feature-to-label, and label-to-label interactions. What further complicates the problem is that not all features are useful, and some can deteriorate the classification performance. Sparsity-based methods have been widely used to address multi-label feature selection due to their efficiency and effectiveness. However, most (if not all) existing methods do not consider the three forms of interactions simultaneously, which could hinder their ability to achieve good performance. Moreover, most existing methods are gradient-based, which are prone to getting stuck at local optima. This paper proposes a new sparsity-based feature selection approach that can simultaneously consider all three forms of interactions. Furthermore, this paper develops a novel sparse learning method based on particle swarm optimisation that can avoid local optima. The proposed method is compared against the state-of-the-art multi-label feature selection methods in terms of multi-label classification performance. The results show that our method performed significantly better in selecting high-quality feature subsets with respect to various feature subset sizes. [ABSTRACT FROM AUTHOR]

Molecular retrosynthesis is a significant and complex problem in the field of chemistry, however, traditional manual synthesis methods not only need well-trained experts but also are time-consuming. With the development of Big Data and machine learning, artificial intelligence (AI) based retrosynthesis is attracting more attention and has become a valuable tool for molecular retrosynthesis. At present, Monte Carlo tree search is a mainstream search framework employed to address this problem. Nevertheless, its search efficiency is compromised by its large search space. Therefore, this paper proposes a novel approach for retrosynthetic route planning based on evolutionary optimization, marking the first use of Evolutionary Algorithm (EA) in the field of multi-step retrosynthesis. The proposed method involves modeling the retrosynthetic problem into an optimization problem, defining the search space and operators. Additionally, to improve the search efficiency, a parallel strategy is implemented. The new approach is applied to four case products and compared with Monte Carlo tree search. The experimental results show that, in comparison to the Monte Carlo tree search algorithm, EA significantly reduces the number of calling single-step model by an average of 53.9%. The time required to search three solutions decreases by an average of 83.9%, and the number of feasible search routes increases by 1.38 times. The source code is available at https://github.com/ilog-ecnu/EvoRRP. [ABSTRACT FROM AUTHOR]

Heart sound auscultation has been applied in clinical usage for early screening of cardiovascular diseases. Due to the high demand for auscultation expertise, automatic auscultation can help with auxiliary diagnosis and reduce the burden of training professional clinicians. Nevertheless, there is a limit to classic machine learning's performance improvement in the era of Big Data. Deep learning has outperformed classic machine learning in many research fields, as it employs more complex model architectures with a stronger capability of extracting effective representations. Moreover, it has been successfully applied to heart sound analysis in the past years. As most review works about heart sound analysis were carried out before 2017, the present survey is the first to work on a comprehensive overview to summarise papers on heart sound analysis with deep learning published in 2017–2022. This work introduces both classic machine learning and deep learning for comparison, and further offer insights about the advances and future research directions in deep learning for heart sound analysis. [ABSTRACT FROM AUTHOR]

Practitioners of multi-objective optimization currently lack open tools that provide decision support through knowledge discovery. There exist many software platforms for multi-objective optimization, but they often fall short of implementing methods for rigorous post-optimality analysis and knowledge discovery from the generated solutions. This paper presents Mimer, a multi-criteria decision support tool for solution exploration, preference elicitation, knowledge discovery, and knowledge visualization. Mimer is openly available as a web-based tool and uses state-of-the-art web-technologies based on WebAssembly to perform heavy computations on the client-side. Its features include multiple linked visualizations and input methods that enable the decision maker to interact with the solutions, knowledge discovery through interactive data mining and graph-based knowledge visualization. It also includes a complete Python programming interface for advanced data manipulation tasks that may be too specific for the graphical interface. Mimer is evaluated through a user study in which the participants are asked to perform representative tasks simulating practical analysis and decision making. The participants also complete a questionnaire about their experience and the features available in Mimer. The survey indicates that participants find Mimer useful for decision support. The participants also offered suggestions for enhancing some features and implementing new features to extend the capabilities of the tool. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms (EAs) have shown their competitiveness in solving the problem of feature selection. However, limited by their encoding scheme, most of them face the challenge of "curse of dimensionality". To address the issue, in this paper, an objective space constraint-based evolutionary algorithm, named OSC-EA, is proposed for high-dimensional feature selection (HDFS). Although the decision space of EAs for HDFS is very huge, its objective space is the same as that of the low-dimensional feature selection. Based on this fact, in the proposed OSC-EA, the HDFS is firstly modeled as a constrained problem, where a constraint of the objective space is introduced and used to partition the whole objective space into the "feasible region" and the "infeasible region". To handle the constrained problem, a two-stage $\varepsilon$ɛ constraint-based evolutionary scheme is designed. In the first stage, the value of $\varepsilon$ɛ is set to be very small, which ensures that the search concentrates on the "feasible region", and the latent high-quality feature subsets can be found quickly. Then, in the second stage, the value of $\varepsilon$ɛ increases gradually, so that more solutions in the "infeasible region" are considered. Until the end of the scheme, $\varepsilon \rightarrow \infty$ɛ→∞; all the solutions in the objective space are considered. By using the search in the second stage, the quality of the obtained feature subsets is further improved. The empirical results on different high-dimensional datasets demonstrate the effectiveness and efficiency of the proposed OSC-EA. [ABSTRACT FROM AUTHOR]

Gasoline blending scheduling uses resource allocation and operation sequencing to meet a refinery's production requirements. The presence of nonlinearity, integer constraints, and a large number of decision variables adds complexity to this problem, posing challenges for traditional and evolutionary algorithms. This paper introduces a novel multiobjective optimization approach driven by a diffusion model (named DMO), which is designed specifically for gasoline blending scheduling. To address integer constraints and generate feasible schedules, the diffusion model creates multiple intermediate distributions between Gaussian noise and the feasible domain. Through iterative processes, the solutions transition from Gaussian noise to feasible schedules while optimizing the objectives using the gradient descent method. DMO achieves simultaneous objective optimization and constraint adherence. Comparative tests are conducted to evaluate DMO's performance across various scales. The experimental results demonstrate that DMO surpasses state-of-the-art multiobjective evolutionary algorithms in terms of efficiency when solving gasoline blending scheduling problems. [ABSTRACT FROM AUTHOR]

This paper provides an accessible explanation of the working mechanism of Monte-Carlo Tree Search, an influential search algorithm. The paper summarizes the procedure of Monte-Carlo Tree Search, including selection, expansion, simulation, and backpropagation. Additionally, immersive examples based on Tic-Tac-Toe, Go, and Sokoban, two two-player competitive games and a classic single-player puzzle game, are presented to illustrate how Monte-Carlo Tree Search works. The full article with interactive contents is published on IEEE Xplore. [ABSTRACT FROM AUTHOR]

In this paper, the idea of a fuzzy color and a fuzzy color space is shown in an interactive way. It is proposed several interactive elements, where readers can understand the different steps to build them. Furthermore, these elements allow the user to test with his/her own images via the behavior of the fuzzy colors. [ABSTRACT FROM AUTHOR]

Thanks to the many submissions we have received, we can present this second part of our special issue on "AI-eXplained." In here, we continue our mission to demystify the intricate world of artificial intelligence and make it accessible to a broader audience. As AI continues to evolve and integrate into various aspects of our lives, it becomes increasingly important to bridge the gap between experts and those eager to understand the inner workings of AI systems. Our goal remains unchanged—to present AI concepts in a comprehensible, engaging, and interactive manner, empowering our readers to explore and grasp the enchanting world of AI. [ABSTRACT FROM AUTHOR]

Recently, contrastive learning has shown its effectiveness in self-supervised learning by training features of augmentations of input images based on the contrastive loss. This paper aims to introduce contrastive learning and discuss the effects of augmentations, features, and parameters of contrastive learning. Interactive figures are developed to demonstrate the effective schemes proposed in contrastive learning and can be accessed on IEEE Xplore. [ABSTRACT FROM AUTHOR]

Fuzzy systems are known to provide not only accurate but also interpretable predictions. However, their explainability may be undermined if non-semantically grounded linguistic terms are used. Additional non-trivial challenges would arise if a prediction were to be explained counterfactually, i.e., in terms of hypothetical, non-predicted outputs. In this paper, we explore how both factual and counterfactual automated explanations can justify the output of fuzzy rule-based classifiers, and thus contribute to making them more trustworthy. Moreover, we demonstrate how end user preferences can be handled by customizing automated explanations, making them interactive, personalized, and therefore, human-centric. The full immersive article at IEEE Xplore provides detailed interactive examples for better understanding. [ABSTRACT FROM AUTHOR]

The IEEE Standards Association (SA) officially published the XES Standard as IEEE Std 1849-2016: IEEE Standard for eXtensible Event Stream (XES) for Achieving Interoperability in Event Logs and Event Streams on 11 November 2016. This standard has been sponsored by the IEEE Computational Intelligence Society (CIS) Standards Committee. Through the XES Standard, event data can be transported from the system where it was generated to the system in which it can be stored and analyzed, without losing semantics. Next to providing a standardized syntax and semantics, the XES Standard also supports the introduction of new extensions to define additional concepts in a flexible manner. The standard allows for the exchange of event data between different process mining tools. [ABSTRACT FROM AUTHOR]

The 2024 IEEE World Congress on Computational Intelligence (IEEE WCCI1 2024) began with this congratulation in the Opening Ceremony on July 1st, 2024, in Yokohama, Japan, as shown in Figure 1. This IEEE Computational Intelligence Society (CIS) flagship conference started in 1994 in Orlando, Florida, USA, to enhance the interdisciplinary discussion and cooperation by calling together the people in neural networks, fuzzy systems, evolutionary computation and related computational intelligence areas. At the beginning, it was a quadrennial event, with annually held individual conferences, namely, International Joint Conference on Neural Networks (IJCNN), IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) and IEEE Congress on Evolutionary Computation (IEEE CEC). After 2008, it has been held every two years constantly. [ABSTRACT FROM AUTHOR]

Despite the recent advancements in dialogue systems, persona-driven chatbots are still in their infancy. Previous studies on persona-driven dialogue generation demonstrated its ability in generating responses that contain more detailed persona information. However, the challenge of maintaining persona consistency and contextual coherence still persists in persona-driven dialogue generation. Moreover, current methods have limitations in processing multi-source inputs and identifying interlocutor intents due to the absence of trustworthy labels and effective modeling. Additionally, numerous approaches rely on pre-trained large-scale language models that require costly computational resources. To address these challenges, a lightweight hierarchical intent-inferring pointer network is proposed for multi-source persona-driven dialogue generation. The proposed method involves detecting interlocutor intents in chitchat and utilizing pseudo labeling and natural language inference techniques to generate intent labels. Our model is evaluated on a benchmark dataset PersonaChat. The experimental results show that our model outperforms the strongest baseline by 13.47% and 4.28% in terms of persona consistency and contextual coherence, respectively. [ABSTRACT FROM AUTHOR]

Large variations across images make image classification a challenging task; limited training data further increases its difficulty. Genetic programming (GP) has been considerably applied to image classification. However, most GP methods tend to directly evolve a single classifier or depend on a predefined classification algorithm, which typically does not lead to ideal generalization performance when only a few training instances are available. Applying ensemble learning to classification often outperforms employing a single classifier. However, single-tree representation (each individual contains a single tree) is widely employed in GP. Training multiple diverse and accurate base learners/classifiers based on single-tree GP is challenging. Therefore, this article proposes a new ensemble construction method based on multi-tree GP (each individual contains multiple trees) for image classification. A single individual forms an ensemble, and its multiple trees constitute base learners. To find the best individual in which multiple trees are diverse and effectively cooperate, i.e., the nth tree can correct the errors of the previous n-1 trees, the new method assigns different weights to multiple trees using the idea of AdaBoost and performs classification via weighted majority voting. Furthermore, a new tree representation is developed to evolve diverse and accurate base learners that extract useful features and conduct classification simultaneously. The new approach achieves significantly better performance than almost all benchmark methods on eight datasets. Additional analyses highlight the effectiveness of the new ensembles and tree representation, demonstrating the potential for providing valuable interpretability in ensemble trees. [ABSTRACT FROM AUTHOR]

"The increased complexity and intelligence of automation systems require the development of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected space, this study develops explainable data-driven IFD approaches for nonlinear dynamic systems. More specifically, we parameterize nonlinear systems through a generalized kernel representation for system modeling and the associated fault diagnosis. An important result obtained is a unified form of kernel representations, applicable to both unsupervised and supervised learning. More importantly, through a rigorous theoretical analysis, we discover the existence of a bridge (i.e., a bijective mapping) between some supervised and unsupervised learning-based entities. Notably, the designed IFD approaches achieve the same performance with the use of this bridge. In order to have a better understanding of the results obtained, both unsupervised and supervised neural networks are chosen as the learning tools to identify the generalized kernel representations and design the IFD schemes; an invertible neural network is then employed to build the bridge between them. This article is a perspective article, whose contribution lies in proposing and formalizing the fundamental concepts for explainable intelligent learning methods, contributing to system modeling and data-driven IFD designs for nonlinear dynamic systems." [ABSTRACT FROM AUTHOR]

Early skin cancer diagnosis saves lives as the disease can be successfully treated through complete excision. Computer-aided diagnosis methods are developed using artificial intelligence techniques to help earlier detection and identify hidden causes leading to cancers in skin lesion images. In skin cancer image classification problems, an ensemble of classifiers has demonstrated better classification ability than a single classification algorithm. Traditionally, training an ensemble uses the complete set of original features, where some of these features can be redundant or irrelevant and hence, may not provide useful information in generating good models for ensemble classification. Moreover, newly created features may help improve classification performance. To address this issue, the existing methods have used feature construction for building an ensemble classifier, which usually creates a fixed number of features that may fit the training data too well, resulting in poor test performance. This study develops a novel classification approach that combines ensemble learning, feature selection, and feature construction utilizing genetic programming (GP) to handle the above limitations. The proposed method automatically evolves variable-length feature vectors consisting of GP-selected and GP-constructed features suitable for training an ensemble classifier. This study evaluates the effectiveness of the proposed method on two benchmark real-world skin image datasets that include dermoscopy and standard camera images. The experimental results reveal that the proposed algorithm significantly outperforms four state-of-the-art convolutional neural network methods, the existing GP approaches, and 11 commonly used machine learning methods. Furthermore, this study also includes interpreting evolved individuals that highlight important skin cancer characteristics playing a vital role in discriminating images of different cancer classes. This study shows that high classification performance can be achieved at a low cost of computational resources and inference time, and accordingly, this method is potentially suitable to be implemented in mobile devices for the automated screening of skin lesions and many other malignancies in low-resource settings. [ABSTRACT FROM AUTHOR]

I am deeply honored to serve as the President of the IEEE Computational Intelligence Society (CIS) for 2024-2025. I had never imagined that I would become the President of our society when I joined IEEE at the 1998 World Congress on Computational Intelligence. I would take this opportunity to thank Bernadette Bouchon-Meunier, chair of the nomination committee and her colleagues, for their trust. I am also very much grateful to Jim Keller, the President in 2022-2023 and now Past President, who mentored me through the year when I was the President-Elect. My thanks also go to Tom Compton, the Executive Director, whose support has been always very helpful and timely. [ABSTRACT FROM AUTHOR]

In this technical survey, the latest advancements in the field of recommender systems are comprehensively summarized. The objective of this study is to provide an overview of the current state-of-the-art in the field and highlight the latest trends in the development of recommender systems. It starts with a comprehensive summary of the main taxonomy of recommender systems, including personalized and group recommender systems. In addition, the survey analyzes the robustness, data bias, and fairness issues in recommender systems, summarizing the evaluation metrics used to assess the performance of these systems. Finally, it provides insights into the latest trends in the development of recommender systems and highlights the new directions for future research in the field. [ABSTRACT FROM AUTHOR]

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Neuromorphic computing is an emerging research field that aims to develop new intelligent systems by integrating theories and technologies from multiple disciplines, such as neuroscience, deep learning and microelectronics. Various software frameworks have been developed for related fields, but an efficient framework dedicated to spike-based computing models and algorithms is lacking. In this work, we present a Python-based spiking neural network (SNN) simulation and training framework, named SPAIC, that aims to support brain-inspired model and algorithm research integrated with features from both deep learning and neuroscience. To integrate different methodologies from multiple disciplines and balance flexibility and efficiency, SPAIC is designed with a neuroscience-style frontend and a deep learning-based backend. Various types of examples are provided to demonstrate the wide usability of the framework, including neural circuit simulation, deep SNN learning and neuromorphic applications. As a user-friendly, flexible, and high-performance software tool, it will help accelerate the rapid growth and wide applicability of neuromorphic computing methodologies. [ABSTRACT FROM AUTHOR]

"Large-scale multiobjective optimization problems (LSMOPs) are characterized as optimization problems involving hundreds or even thousands of decision variables and multiple conflicting objectives. To solve LSMOPs, some algorithms designed a variety of strategies to track Pareto-optimal solutions (POSs) by assuming that the distribution of POSs follows a low-dimensional manifold. However, traditional genetic operators for solving LSMOPs have some deficiencies in dealing with the manifold, which often results in poor diversity, local optima, and inefficient searches. In this work, a generative adversarial network (GAN)-based manifold interpolation framework is proposed to learn the manifold and generate high-quality solutions on the manifold, thereby improving the optimization performance of evolutionary algorithms. We compare the proposed approach with several state-of-the-art algorithms on various large-scale multiobjective benchmark functions. The experimental results demonstrate that significant improvements have been achieved by the proposed framework in solving LSMOPs." [ABSTRACT FROM AUTHOR]