Improving the performance of multi-objective genetic algorithm for function approximation through parallel islands specialisation

Authors:
A. Guillén;I. Rojas;J. González;H. Pomares;L. J. Herrera;B. Paechter
Affiliations:
Department of Computer Architecture and Computer Technology, Universidad de Granada, Spain;Department of Computer Architecture and Computer Technology, Universidad de Granada, Spain;Department of Computer Architecture and Computer Technology, Universidad de Granada, Spain;Department of Computer Architecture and Computer Technology, Universidad de Granada, Spain;Department of Computer Architecture and Computer Technology, Universidad de Granada, Spain;School of Computing, Napier University, Scotland
Venue:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

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Abstract

Nature shows many examples where the specialisation of elements aimed to solve different problems is successful. There are explorer ants, worker bees, etc., where a group of individuals is assigned a specific task. This paper will extrapolate this philosophy, applying it to a multiobjective genetic algorithm. The problem to be solved is the design of Radial Basis Function Neural Networks (RBFNNs) that approximate a function. A non distributed multiobjective algorithm will be compared against a parallel approach that emerges as a straight forward specialisation of the crossover and mutation operators in different islands. The experiments will show how, as in the real world, if the different islands evolve specific aspects of the RBFNNs, the results are improved.