Distributed population based incremental learning

Authors:
Rui Rodrigues
Affiliations:
Departamento de Electrotecnia, Faculdade Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal
Venue:
EC'05 Proceedings of the 6th WSEAS international conference on Evolutionary computing
Year:
2005

Citing 3
Cited 0

Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms on LAN-message Passing Architectures using PVM: Application to the Routing Problem

PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning

Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning

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Abstract

Probabilistic model building genetic algorithms (PMBGA) are powerful search techniques that are used successfully to solve hard computational problems. We exploit the natural bridge island concept in the Parallel PMBGA context. Bridge Island is an island that all the best elements arriving from evolving islands have to pass before its migration to other islands occurs. Some ways of best elements selection to migrate and also some ways of merging information between the migrating element and the receiver element will be tested. The main objective is to compare the relative performance between distributed PBIL and isolated PBIL. The parallel models that will be used are the master-slave topology, island model and coarse-grained.