Adaptive operator selection with reinforcement learning

DOI10.1016/J.INS.2021.10.025OpenAlexW3203930809MaRDI QIDQ6139514

Ibrahim Atli, Mehmet Emin Aydin, Rafet Durgut

Publication date: 19 January 2024

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2021.10.025

zbMATH Keywords

reinforcement learningQ-learningartificial bee colonyadaptive operator selectionclustering-based Q-learning

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Computational learning theory (68Q32) Learning and adaptive systems in artificial intelligence (68T05) Numerical solutions to equations with nonlinear operators (65J15) Computational aspects of data analysis and big data (68T09)

Cites Work

• Autonomous operator management for evolutionary algorithms
• Analyzing bandit-based adaptive operator selection mechanisms
• Application of adaptive reliability importance sampling-based extended domain PSO on single mode failure in reliability engineering
• A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm
• Enhancing MOEA/D with information feedback models for large-scale many-objective optimization
• An improved MOEA/D algorithm with an adaptive evolutionary strategy
• Finite-time analysis of the multiarmed bandit problem
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