Computational Intelligence Research Research Articles

Hybrid Topological Data Analysis and Deep Learning for Basal Cell Carcinoma Diagnosis.

A critical clinical indicator for basal cell carcinoma (BCC) is the presence of telangiectasia (narrow, arborizing blood vessels) within the skin lesions. Many skin cancer imaging processes today exploit deep learning (DL) models for diagnosis, segmentation of features, and feature analysis. To extend automated diagnosis, recent computational intelligence research has also explored the field of Topological Data Analysis (TDA), a branch of mathematics that uses topology to extract meaningful information from highly complex data. This study combines TDA and DL with ensemble learning to create a hybrid TDA-DL BCC diagnostic model. Persistence homology (a TDA technique) is implemented to extract topological features from automatically segmented telangiectasia as well as skin lesions, and DL features are generated by fine-tuning a pre-trained EfficientNet-B5 model. The final hybrid TDA-DL model achieves state-of-the-art accuracy of 97.4% and an AUC of 0.995 on a holdout test of 395 skin lesions for BCC diagnosis. This study demonstrates that telangiectasia features improve BCC diagnosis, and TDA techniques hold the potential to improve DL performance.

Bus Bridging for Rail Disruptions: A Distributionally Robust Fuzzy Optimization Approach

Dealing with uncertain rail disruptions effectively raises a significant challenge for computational intelligence research. This article studies the bus bridging problem under demand uncertainty, where the passenger demand is represented as parametric interval-valued fuzzy variables and their associated uncertainty distribution sets. A distributionally robust fuzzy optimization model is proposed to minimize the maximum travel time and to search for the optimal scheme for vehicle allocation, route selection, and frequency determination. To solve the proposed robust model, we discuss the computational issues concerning credibilistic constraints, turning the robust counterpart model into computationally tractable equivalent formulations. The proposed approach is verified, and the resulting method is validated with a report on uncertain parameters in a real-world disrupted event of Shanghai Rail Line 1. Experimental results show that the distributionally robust fuzzy optimization approach can provide a better uncertainty-immunized solution.

Impact of Boundary Control Methods on Bound-Constrained Optimization Benchmarking

Benchmarking various metaheuristics and their new enhancements, strategies, and adaptation mechanisms has become standard in computational intelligence research. Recently, many challenges and issues regarding fair comparisons and recommendations toward good practices for benchmarking of metaheuristic algorithms, have been identified. This article is aimed at an important issues in metaheuristics design and benchmarking, which are boundary strategies or boundary control methods (BCMs). This work aims to investigate whether the choice of a BCM could significantly influence the performance of competitive algorithms. The experiments encompass the top three performing algorithms from IEEE CEC competitions 2017 and 2020 with six different BCMs. We provide extensive statistical analysis and rankings resulting in conclusions and recommendations for metaheuristics researchers and possibly also for the future direction of benchmark definitions. We conclude that the BCM should be considered another vital metaheuristics input variable for unambiguous reproducibility of results in benchmarking and for a better understanding of population dynamics, since the BCM setting could impact the optimization method performance.

Data Enhancement of Underwater High-Speed Vehicle Echo Signals Based on Improved Generative Adversarial Networks

Underwater target recognition is currently one of the hottest topics in computational intelligence research. However, underwater target recognition tasks based on deep learning techniques are difficult to conduct due to the shortage of acoustic echo signal samples, which results in poor training performance for existing deep learning models. Generative adversarial networks (GANs) have been widely used in data enhancement and image generation, providing a novel strategy for dealing with challenges in the research field mentioned above. To address the insufficiency of echo signal data for underwater high-speed vehicles, this paper proposes an underwater echo signal data enhancement method that uses an improved GAN based on convolution units for small sample sizes. First, we take pool test data as the training sample input and carry out data standardization, data interception, and copy-related processing work. Secondly, this paper proposes an improved generative adversarial network underwater (IGAN-UW) model to generate underwater echo signals. Finally, a CNN model combines the generated data with the original data to conduct classification training for underwater targets. Experimental results show that the IGAN-UW model is suitable for the generation of highly realistic original echo signals in cases with small sample sizes, providing a new approach to the active detection and recognition of underwater targets.

Automating Predictive Toxicology Using ComptoxAI.

ComptoxAI is a new data infrastructure for computational and artificial intelligence research in predictive toxicology. Here, we describe and showcase ComptoxAI's graph-structured knowledge base in the context of three real-world use-cases, demonstrating that it can rapidly answer complex questions about toxicology that are infeasible using previous technologies and data resources. These use-cases each demonstrate a tool for information retrieval from the knowledge base being used to solve a specific task: The "shortest path" module is used to identify mechanistic links between perfluorooctanoic acid (PFOA) exposure and nonalcoholic fatty liver disease; the "expand network" module identifies communities that are linked to dioxin toxicity; and the quantitative structure-activity relationship (QSAR) dataset generator predicts pregnane X receptor agonism in a set of 4,021 pesticide ingredients. The contents of ComptoxAI's source data are rigorously aggregated from a diverse array of public third-party databases, and ComptoxAI is designed as a free, public, and open-source toolkit to enable diverse classes of users including biomedical researchers, public health and regulatory officials, and the general public to predict toxicology of unknowns and modes of action.

Genetic-GNN: Evolutionary architecture search for Graph Neural Networks

Neural architecture search (NAS) has seen significant attention throughout the computational intelligence research community and has pushed forward the state-of-the-art of many neural models to address grid-like data such as texts and images. However, little work has been done on Graph Neural Network (GNN) models dedicated to unstructured network data. Given the huge number of choices and combinations of components such as aggregators and activation functions, determining the suitable GNN model for a specific problem normally necessitates tremendous expert knowledge and laborious trials. In addition, the moderate change of hyperparameters such as the learning rate and dropout rate would dramatically impact the learning capacity of a GNN model. In this paper, we propose a novel framework through the evolution of individual models in a large GNN architecture searching space. Instead of simply optimizing the model structures, an alternating evolution process is performed between GNN model structures and hyperparameters to dynamically approach the optimal fit of each other. Experiments and validations demonstrate that evolutionary NAS is capable of matching existing state-of-the-art reinforcement learning methods for both transductive and inductive graph representation learning and node classification.

A deep reinforcement learning based hyper-heuristic for combinatorial optimisation with uncertainties

In the past decade, considerable advances have been made in the field of computational intelligence and operations research. However, the majority of these optimisation approaches have been developed for deterministically formulated problems, the parameters of which are often assumed perfectly predictable prior to problem-solving. In practice, this strong assumption unfortunately contradicts the reality of many real-world problems which are subject to different levels of uncertainties. The solutions derived from these deterministic approaches can rapidly deteriorate during execution due to the over-optimisation without explicit consideration of the uncertainties. To address this research gap, a deep reinforcement learning based hyper-heuristic framework is proposed in this paper. The proposed approach enhances the existing hyper-heuristics with a powerful data-driven heuristic selection module in the form of deep reinforcement learning on parameter-controlled low-level heuristics, to substantially improve their handling of uncertainties while optimising across various problems. The performance and practicality of the proposed hyper-heuristic approach have been assessed on two combinatorial optimisation problems: a real-world container terminal truck routing problem with uncertain service times and the well-known online 2D strip packing problem. The experimental results demonstrate its superior performance compared to existing solution methods for these problems. Finally, the increased interpretability of the proposed deep reinforcement learning hyper-heuristic has been exhibited in comparison with the conventional deep reinforcement learning methods.

Polarization and Persuasion as Opposite Integration Strategies in Collective Models

In this paper, we consider the known opinion formation problem from the point of view of computational collective intelligence research, with consideration to some sociological research in the area of social influence. We study two approaches to opinion formation – polarization and persuasion – as viewed from the point of view of our collective model (as opinion integration strategies). We detail both approaches and then conduct simulational experiments to study them both in single and averaged over multiple runs. We found special interest in the polarization strategy, which we want to use in the future as a basis for a new measure of collectives behavior.

PREFACE

It is with great pleasure that I introduce the proceedings of the 2019 High Performance Computing and Computational Intelligence Conference (HPCCI 2019) which was held recently Chengdu, China during October 25-27, 2019. HPCCI 2019 was dedicated to technical issues related to High Performance Computing and Computational Intelligence.High performance computing represents the national strength of a country and plays an essential supporting role in the development of national science and technology. Quantum computing, as a new computing method, could be an emerging area of high-performance computing in the future. The development of high performance computing may have some breakthroughs in these areas. At the same time, it is found that the implicit parallelism provided by the quantum system improves the performance of the algorithm. Quantum theory may be an important research direction to construct the basic theoretical system for some intelligence schemes in the future. The development of computational intelligence cannot be separated from the support of high-performance computing. The development of the two subject areas has gradually formed an integrated system. Based on it, the objectives of the conference were not only to provide opportunities to the international scientists to present state of the art research in High Performance Computing and Computational Intelligence but also to provide a forum to discuss ideas for future directions in the field and explore possibilities for collaborative research projects.The conference program included keynote, oral, and poster presentations from scholars working in the areas of High Performance Computing and Computational Intelligence from all over the world. It covered recent trends and progress made in the field of High Performance Computing and Computational Intelligence. Keynote lectures regarding the latest information in their respective areas of expertise were shared by experts.The proceedings present a selection of high quality papers submitted to the conference by researchers from universities, research institutes, and industry. All papers were subjected to peer-review by conference committee members and international reviewers. The papers were selected based on their quality and their relevance to the conference. The proceedings present recent advances in the fields of High performance Computing and Computing Intelligence.I would like to express special gratitude to members of the conference committee and organizers of the conference. I would also like to thank the reviewers for their valuable time and advice which helped in improving the quality of the papers selected for presentation at the conference and for publication in the proceedings. Finally I want to thank the authors, the members of the organizing committee, the reviewers, the chairpersons, sponsors, and all other conference participants for their support of HPCCI 2019.Conference ChairProf. Peng Wang Southwest Minzu University, China

Guest Editorial From Intelligent Control to Smart Management of Cyber-Physical-Social Systems: A Celebration of 70th Anniversary of Cybernetics by Norbert Wiener

Inspired by the idealism embodied in Russell and Whitehead’s “Principia Mathematica,” Wiener marched along a different and unique path toward sciences of intelligence and behavior which culminated at “Cybernetics: Or Control and Communication in the Animal and the Machine” 70 years ago. Since then, we have witnessed the birth of Cognitive Science, Artificial Intelligence (AI), Computational Intelligence, and many other new research fields and disciplines, all of which have been catalyzed by Cybernetics. The IEEE Systems, Man, AND Cybernetics Society and this Transactions on Cybernetics have become the focal point of the broad cybernetics community by promoting the theory, practice, and interdisciplinary aspects of systems science and engineering, human-machine systems, and cybernetics principles. It is a time of celebration and reflection.

Experimental Assessment of Differential Evolution with Grid-Based Parameter Adaptation

Evolutionary algorithms have been long established as an essential field of research in Computational Intelligence. Differential Evolution is placed among the most successful algorithms of this type. However, it has proved to be highly sensitive on its parameters. For this purpose, offline and online parameter-control methods have been proposed. Recently, a grid-based parameter adaptation procedure was introduced and successfully applied on Differential Evolution. Despite the generality of the method, the resulting algorithm was capable to compete with already tuned adaptive algorithms on high-dimensional test problems without any additional preprocessing. The present work extends the experimental study of this approach on the state-of-the-art CEC-2013 test suite. Two variants of the algorithm are considered and different initial conditions are tested to shed light on its performance aspects. Comparisons with other algorithms as well as between the proposed approaches are reported. The results verify the potential of the grid-based parameter adaptation method as a general-purpose alternative for parameter setting.

AnRAD: A Neuromorphic Anomaly Detection Framework for Massive Concurrent Data Streams.

The evolution of high performance computing technologies has enabled the large-scale implementation of neuromorphic models and pushed the research in computational intelligence into a new era. Among the machine learning applications, unsupervised detection of anomalous streams is especially challenging due to the requirements of detection accuracy and real-time performance. Designing a computing framework that harnesses the growing computing power of the multicore systems while maintaining high sensitivity and specificity to the anomalies is an urgent research topic. In this paper, we propose anomaly recognition and detection (AnRAD), a bioinspired detection framework that performs probabilistic inferences. We analyze the feature dependency and develop a self-structuring method that learns an efficient confabulation network using unlabeled data. This network is capable of fast incremental learning, which continuously refines the knowledge base using streaming data. Compared with several existing anomaly detection approaches, our method provides competitive detection quality. Furthermore, we exploit the massive parallel structure of the AnRAD framework. Our implementations of the detection algorithm on the graphic processing unit and the Xeon Phi coprocessor both obtain substantial speedups over the sequential implementation on general-purpose microprocessor. The framework provides real-time service to concurrent data streams within diversified knowledge contexts, and can be applied to large problems with multiple local patterns. Experimental results demonstrate high computing performance and memory efficiency. For vehicle behavior detection, the framework is able to monitor up to 16000 vehicles (data streams) and their interactions in real time with a single commodity coprocessor, and uses less than 0.2 ms for one testing subject. Finally, the detection network is ported to our spiking neural network simulator to show the potential of adapting to the emerging neuromorphic architectures.

Transformations in Shifting Models: Reorienting with DeepDream

We propose a framework for sonic creativity via computational methods and artificial intelligence research. We extend Norman Sieroka’s theory of sound from a 3-fold to a 4-fold hierarchy so that sound now becomes characterised in terms of the acoustic, physiological, speculative and phenomenal layers. We describe how manipulations in the proposed speculative layer can directly act on how one orients the very apprehension of sound. Black box algorithms, in particular deep neural networks as instantiated by Google’s DeepDream, are then discussed as an illustrative example that can be used to manipulate the speculative layer. We caution that DeepDream offers a warning of how easily our tools capture our phenomenal apprehensions, potentially obfuscating what is just beyond our perception in the process.

Application of Artificial Fish Swarm Algorithm in Radial Basis Function Neural Network

Neural network is one of the branches with the most active research, development and application in computational intelligence and machine study. Radial basis function neural network (RBFNN) has achieved some success in more than one application field, especially in pattern recognition and functional approximation. Due to its simple structure, fast training speed and excellent generalization ability, it has been widely used. Artificial fish swarm algorithm (AFSA) is a new swarm intelligent optimization algorithm derived from the study on the preying behavior of fish swarm. This algorithm is not sensitive to the initial value and the parameter selection, but strong in robustness and simple and easy to realize and it also has parallel processing capability and global searching ability. This paper mainly researches the weight and threshold of AFSA in optimizing RBFNN. The simulation experiment proves that AFSA-RBFNN is significantly advantageous in global optimization capability and that it has outstanding global optimization ability and stability.

コンピューテーショナル・インテリジェンス研究の源流をたどる ―NN部会（CI部会）の活動を中心に―

コンピューテーショナル・インテリジェンス研究の源流をたどる ―NN部会（CI部会）の活動を中心に―

A Survey of Evolutionary Computation for Resource Management of Processing in Cloud Computing [Review Article

Cloud computing is significantly reshaping the computing industry. Individuals and small organizations can benefit from using state-of-the-art services and infrastructure, while large companies are attracted by the flexibility and the speed with which they can obtain the services. Service providers compete to offer the most attractive conditions at the lowest prices. However, the environmental impact and legal aspects of cloud solutions pose additional challenges. Indeed, the new cloud-related techniques for resource virtualization and sharing and the corresponding service level agreements call for new optimization models and solutions. It is important for computational intelligence researchers to understand the novelties introduced by cloud computing. The current survey highlights and classifies key research questions, the current state of the art, and open problems.

Learning-Based Procedural Content Generation

Procedural content generation (PCG) has recently become one of the hottest topics in computational intelligence and AI game research. While some substantial progress has been made in this area, there are still several challenges ranging from content evaluation to personalized content generation. In this paper, we present a novel PCG framework based on machine learning, named learning-based procedure content generation (LBPCG), to tackle a number of challenging problems. By exploring and exploiting information gained in game development and public player test, our framework can generate robust content adaptable to end-user or target players on-line with minimal interruption to their gameplay experience. As the data-driven methodology is emphasized in our framework, we develop learning-based enabling techniques to implement the various models required in our framework. For a proof of concept, we have developed a prototype based on the classic open source first-person shooter game, Quake. Simulation results suggest that our framework is promising in generating quality content.

An Intelligent Model for Pairs Trading Using Genetic Algorithms.

Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice.

REVIEW OF INDUSTRIAL APPLICATIONS OF COMPUTATIONAL INTELLIGENCE

Bringing cutting-edge natural computation research into industry is a challenging task, with numerous obstacles to overcome. In this paper I describe a few selected projects that we have developed at the Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA) and I discuss the promising future of Computational Intelligence techniques in industry.

The Emerging "Big Dimensionality"

The world continues to generate quintillion bytes of data daily, leading to the pressing needs for new efforts in dealing with the grand challenges brought by Data. Today, there is a growing consensus among the computational intelligence communities that data volume presents an immediate challenge pertaining to the scalability issue. However, when addressing volume in Data analytics, researchers in the data analytics community have largely taken a one-sided study of volume, which is the Big Instance Size factor of the data. The flip side of volume which is the dimensionality factor of Data, on the other hand, has received much lesser attention. This article thus represents an attempt to fill in this gap and places special focus on this relatively under-explored topic of Big Dimensionality, wherein the explosion of features (variables) brings about new challenges to computational intelligence. We begin with an analysis on the origins of Dimensionality. The evolution of feature dimensionality in the last two decades is then studied using popular data repositories considered in the data analytics and computational intelligence research communities. Subsequently, the state-of-the-art feature selection schemes reported in the field of computational intelligence are reviewed to reveal the inadequacies of existing approaches in keeping pace with the emerging phenomenon of Dimensionality. Last but not least, the curse and blessing of Dimensionality are delineated and deliberated.