Evolutionary Algorithms in Artificial Intelligence
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 September 2026 | Viewed by 30
Special Issue Editor
Interests: artificial intelligence; machine learning; deep learning; data science; data mining; health science; evolutionary algorithms research domains
Special Issue Information
Dear Colleagues,
Evolutionary algorithms have become powerful computational tools for solving complex optimization, learning, and decision-making problems across diverse fields of artificial intelligence. Inspired by natural selection and biological evolution, these algorithms—including genetic algorithms, differential evolution, evolutionary strategies, genetic programming, and swarm-based methods—offer flexible frameworks capable of navigating high-dimensional, nonlinear, and multimodal search spaces. With the rapid advancement of machine learning, deep learning, reinforcement learning, and autonomous intelligent systems, evolutionary approaches are increasingly being integrated to enhance model performance, automate architecture design, optimize hyperparameters, and improve robustness in dynamic environments.
This Special Issue aims to bring together high-quality contributions that explore theoretical advances, algorithmic innovations, hybrid frameworks, and real-world applications of evolutionary algorithms in AI. Topics of interest include novel evolutionary methods, multi-objective optimization, reinforcement learning integrations, explainability and fairness in evolutionary AI, and domain-specific applications in engineering, robotics, finance, business, agriculture, computing, healthcare, and beyond. We welcome original research articles, reviews, and case studies that provide valuable insights and push the boundaries of evolutionary computation in artificial intelligence.
Dr. Mir Md Jahangir Kabir
Guest Editor
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Keywords
- evolutionary algorithms
- artificial intelligence
- optimization
- genetic algorithms
- differential evolution
- swarm intelligence
- reinforcement learning
- machine learning
- multi-objective optimization
- intelligent systems
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