Case-based reasoning for meta-heuristics self-parameterization in a multi-agent scheduling system
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 M...
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| Other Authors: | , |
| Format: | conferenceObject |
| Language: | eng |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://hdl.handle.net/10400.22/1555 |
| Country: | Portugal |
| Oai: | oai:recipp.ipp.pt:10400.22/1555 |
| Summary: | 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. |
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