| Name: | Description: | Size: | Format: | |
|---|---|---|---|---|
| 158.83 KB | Adobe PDF |
Advisor(s)
Abstract(s)
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.
Description
Keywords
Case-based reasoning Learning Metaheuristics Multi-agent systems Scheduling