Computational Intelligence and Evolutionary Algorithms

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 2550

Special Issue Editor


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Guest Editor
Department of Computer Science, School of Physics, Engineering & Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
Interests: computational intelligence; artificial intelligence; evolutionary algorithms; social signal processing

Special Issue Information

Dear Colleagues,

In our ever-changing world, where data are generated rapidly at an exponential rate, there is a need for computationally intelligent algorithms that are capable of quickly and accurately processing data and making good decisions. Automatically processing and analyzing these data, which are generated by social media platforms, commercial transactions, IoT, etc., provide huge advantages to a wide range of sectors, including retail, the automotive industry, defense, finance, manufacturing, etc. In this respect, many researchers have turned their attention to developing and analyzing computational intelligence algorithms. In the design process of CI algorithms, there are many stages in which there appears a need for an optimization algorithm. This includes the optimization of the structure of these algorithms, their parameters, learning weights, etc.

Optimization problems are everywhere and there are many problems that require an optimization algorithm in order to be solved. Inspired by the Darwinian theory of evolution, evolutionary algorithms have been successful in solving a variety of optimization problems. In this respect, this Special Issue addresses novel advances in CI and evolutionary algorithms and their relationships. This journal accepts high-quality studies with original research results and literature review papers in the following fields (by no means exhaustive):

  • Machine Learning;
  • Neural Networks;
  • Deep Learning;
  • Fuzzy Systems;
  • Evolutionary algorithms;
  • Swarm Intelligence;
  • Decision Making.

We look forward to receiving your submissions.

Dr. Mohammadhassan Tayaraninajaran
Guest Editor

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Keywords

  • machine learning
  • neural networks
  • deep learning
  • fuzzy systems
  • evolutionary algorithms
  • swarm intelligence
  • decision making

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Published Papers (2 papers)

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Research

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20 pages, 287 KiB  
Article
Memory-Based Differential Evolution Algorithms with Self-Adaptive Parameters for Optimization Problems
by Shang-Kuan Chen, Gen-Han Wu and Yu-Hsuan Wu
Mathematics 2025, 13(10), 1647; https://doi.org/10.3390/math13101647 - 17 May 2025
Viewed by 164
Abstract
In this study, twelve modified differential evolution algorithms with memory properties and adaptive parameters were proposed to address optimization problems. In the experimental process, these modified differential evolution algorithms were applied to 23 continuous test functions. The results indicate that MBDE2 and IHDE-BPSO3 [...] Read more.
In this study, twelve modified differential evolution algorithms with memory properties and adaptive parameters were proposed to address optimization problems. In the experimental process, these modified differential evolution algorithms were applied to 23 continuous test functions. The results indicate that MBDE2 and IHDE-BPSO3 outperform the original differential evolution algorithm and its extended variants, consistently achieving optimal solutions in most cases. The findings suggest that the proposed improved differential evolution algorithm is highly adaptable across various problems, yielding superior results. Additionally, integrating memory properties significantly enhances the algorithm’s performance and effectiveness. Full article
(This article belongs to the Special Issue Computational Intelligence and Evolutionary Algorithms)

Other

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33 pages, 746 KiB  
Systematic Review
Evolutionary Reinforcement Learning: A Systematic Review and Future Directions
by Yuanguo Lin, Fan Lin, Guorong Cai, Hong Chen, Linxin Zou, Yunxuan Liu and Pengcheng Wu
Mathematics 2025, 13(5), 833; https://doi.org/10.3390/math13050833 - 2 Mar 2025
Cited by 1 | Viewed by 1806
Abstract
In response to the limitations of reinforcement learning and Evolutionary Algorithms (EAs) in complex problem-solving, Evolutionary Reinforcement Learning (EvoRL) has emerged as a synergistic solution. This systematic review aims to provide a comprehensive analysis of EvoRL, examining the symbiotic relationship between EAs and [...] Read more.
In response to the limitations of reinforcement learning and Evolutionary Algorithms (EAs) in complex problem-solving, Evolutionary Reinforcement Learning (EvoRL) has emerged as a synergistic solution. This systematic review aims to provide a comprehensive analysis of EvoRL, examining the symbiotic relationship between EAs and reinforcement learning algorithms and identifying critical gaps in relevant application tasks. The review begins by outlining the technological foundations of EvoRL, detailing the complementary relationship between EAs and reinforcement learning algorithms to address the limitations of reinforcement learning, such as parameter sensitivity, sparse rewards, and its susceptibility to local optima. We then delve into the challenges faced by both reinforcement learning and EvoRL, exploring the utility and limitations of EAs in EvoRL. EvoRL itself is constrained by the sampling efficiency and algorithmic complexity, which affect its application in areas like robotic control and large-scale industrial settings. Furthermore, we address significant open issues in the field, such as adversarial robustness, fairness, and ethical considerations. Finally, we propose future directions for EvoRL, emphasizing research avenues that strive to enhance self-adaptation, self-improvement, scalability, interpretability, and so on. To quantify the current state, we analyzed about 100 EvoRL studies, categorizing them based on algorithms, performance metrics, and benchmark tasks. Serving as a comprehensive resource for researchers and practitioners, this systematic review provides insights into the current state of EvoRL and offers a guide for advancing its capabilities in the ever-evolving landscape of artificial intelligence. Full article
(This article belongs to the Special Issue Computational Intelligence and Evolutionary Algorithms)
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