Guest Editors
Dr. Haichuan Yang
Email: you.kaisen@tokushima-u.ac.jp
Affiliation: Graduate School of Technology, Industrial and Social Sciences, Tokushima University, Tokushima, Japan
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Research Interests: evolutionary algorithm, complex system, optimization, causal inference in statistics
Prof. Chaofeng Zhang
Email: zhang-chaofeng@aiit.ac.jp
Affiliation: School of Information and Electronic Engineering, Advanced Institute of Industrial Technology, Tokyo, Japan
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Research Interests: wireless communications, cloud computing, Internet of Things, cyber-physical system, mobile computing
Dr. Xin Kang
Email: kang-xin@tokushima-u.ac.jp
Affiliation: Department of Computer Science, Tokushima University, Tokushima, Japan
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Research Interests: affective computing, neuro-symbolic AI, multimodal machine learning, knowledge engineering
Dr. Yifei Yang
Email: yyf7236@hirosaki-u.ac.jp
Affiliation: Faculty of Science and Technology, Hirosaki University, Hirosaki, Japan
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Research Interests: evolutionary algorithm, complex system, new energy
Dr. Cheng Tang
Email: tang@limu.ait.kyushu-u.ac.jp
Affiliation: Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan
Homepage:
Research Interests: medical data mining, computer vision, time series forecasting, natural language processing, learning analytics, AI for drug design, optimization
Summary
1) Issue Introduction: Background and Importance
Evolutionary algorithms and complex systems play a critical role in modern artificial intelligence, particularly for addressing large-scale, nonlinear, and dynamic optimization problems. Evolutionary computation offers robust population-based search and adaptation mechanisms, while complex systems theory provides essential insights into nonlinear interactions, adaptive dynamics, and system-level behaviors. Their integration has become increasingly important for understanding and designing intelligent systems capable of operating under uncertainty and complexity.
2) Aim and Scope of the Special Issue
This Special Issue aims to present recent theoretical and methodological advances in evolutionary algorithms and complex systems within artificial intelligence. The scope includes algorithm design, theoretical analysis, hybrid intelligent systems, and applications that exploit evolutionary processes and complex system dynamics. Contributions that enhance scalability, adaptability, robustness, or interpretability of evolutionary and complex computational models are particularly encouraged.
3) Suggested Themes
Evolutionary algorithms and population-based optimization
Complex systems and nonlinear dynamical modeling
Adaptive and self-organizing computational systems
Hybrid frameworks combining evolution and learning
Real-world applications of evolutionary and complex systems
Keywords
evolutionary algorithms, complex systems, population-based optimization, nonlinear dynamics, adaptive systems, self-organization, hybrid intelligent systems, evolutionary computation
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