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• W434719937 abstract "A novel agent-based approach to Meta-Heuristics self-configuration is proposed in this work. Meta-heuristics are examples of algorithms where parameters need to be set up as efficient as possible in order to unsure its performance. This paper presents a learning module for self-parameterization of Meta-heuristics (MHs) in a Multi-Agent System (MAS) for resolution of scheduling problems. The learning is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. In the end, some conclusions are reached and future work outlined. KeywordsCase-based Reasoning, Learning, Metaheuristics, Multi-Agent Systems, Scheduling" @default.
• W434719937 created "2016-06-24" @default.
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• W434719937 date "2011-01-01" @default.
• W434719937 modified "2023-09-27" @default.
• W434719937 title "Case-based reasoning for meta-heuristics self-parameterization in a multi-agent scheduling system" @default.
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