DocumentCode :
2424367
Title :
A Multi-agent Approach to the Adaptation of Migration Topology in Island Model Evolutionary Algorithms
Author :
Lopes, Rodolfo A. ; Silva, Rodrigo C Pedrosa ; Campelo, Felipe ; Guimarães, Frederico G.
Author_Institution :
Programa de Pos-Grad. em Eng. Eletr., Univ. Fed. de Minas Gerais, Belo Horizonte, Brazil
fYear :
2012
fDate :
20-25 Oct. 2012
Firstpage :
160
Lastpage :
165
Abstract :
The Island Model (IM) is an important multi-population approach for improving the performance of Evolutionary Algorithms (EAs) when solving complex problems. One of the critical parameters for defining a suitable IM is the migration topology, which determines the migratory flows between sub-populations of the model. Despite the importance of this parameter, the majority of topologies tend to be naive and fail to take into account the underlying optimization process. To deal with the problem of adequately setting a migration topology, we propose an approach in which the Island Model is transformed into a Multi-Agent System (MAS) capable of learning and adapting the inter-island links based on the experience obtained during the evolutionary process. This approach is compared against two other traditional topologies applied to island versions of two different EAs, and to their usual implementations. The preliminary results strongly suggest an advantage of the IM versions over the original algorithms, and the competitiveness of the proposed approach.
Keywords :
evolutionary computation; learning (artificial intelligence); multi-agent systems; optimisation; EA; IM; MAS; evolutionary process; interisland link adaptation; interisland link learning; island model evolutionary algorithms; migration topology adaptation; multiagent approach; multipopulation approach; optimization process; Adaptation models; Evolutionary computation; Network topology; Optimization; Sociology; Statistics; Topology; Island Model; Migration Topologies; Multi-agent Evolutionary Algorithms; Parallel Evolutionary Algorithms; Reinforcement Learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (SBRN), 2012 Brazilian Symposium on
Conference_Location :
Curitiba
ISSN :
1522-4899
Print_ISBN :
978-1-4673-2641-4
Type :
conf
DOI :
10.1109/SBRN.2012.36
Filename :
6374842
Link To Document :
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