Abstract
This paper proposes a novel agent-based approach to Meta-Heuristics self-configuration. Meta-heuristics are algorithms with parameters which need to be set up as efficient as possible in order to unsure its performance. A learning module for self-parameterization of Meta-heuristics (MH) in a Multi-Agent System (MAS) for resolution of scheduling problems is proposed in this work. The learning module 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. Finally, some conclusions are reached and future work outlined.
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Acknowledgments
This work is supported by a PhD scholarship (SFRH/BD/63404/2009) by FCT “Fundação para a Ciência e a Tecnologia”, FEDER Funds through the “Programa Operacional Factores de Competitividade – COMPETE” program and by National Funds through FCT “Fundação para a Ciência e a Tecnologia” under the project: FCOMP-01-0124-FEDER-PEst-OE/EEI/UI0760/2011.
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Pereira, I., Madureira, A., de Moura Oliveira, P. (2013). Meta-heuristics Self-Parameterization in a Multi-agent Scheduling System Using Case-Based Reasoning. In: Madureira, A., Reis, C., Marques, V. (eds) Computational Intelligence and Decision Making. Intelligent Systems, Control and Automation: Science and Engineering, vol 61. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4722-7_10
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DOI: https://doi.org/10.1007/978-94-007-4722-7_10
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