Evolutionary optimization: pitfalls and booby traps

DOI10.1007/s11390-012-1274-4zbMath1279.68281OpenAlexW2163286715MaRDI QIDQ389022

Raymond Chiong, Thomas Weise, Ke Tang

Publication date: 17 January 2014

Published in: Journal of Computer Science and Technology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11390-012-1274-4

zbMATH Keywords

optimizationmeta-heuristicsevolutionary computingproblem difficulty

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Research exposition (monographs, survey articles) pertaining to computer science (68-02) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (5)

An ensemble algorithm with self-adaptive learning techniques for high-dimensional numerical optimization ⋮ Altruistic population algorithm: a metaheuristic search algorithm for solving multimodal multi-objective optimization problems ⋮ Reservoirs learn to learn ⋮ Global versus local search: the impact of population sizes on evolutionary algorithm performance ⋮ A set of new multi- and many-objective test problems for continuous optimization and a comprehensive experimental evaluation

Uses Software

• CIXL2
• Genocop
• dgpf
• SIGOA
• MSOPS-II

Cites Work

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