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Improving the Performance of Multi-objective Genetic Algorithm for Function Approximation Through Parallel Islands Specialisation

  • A. Guillén
  • I. Rojas
  • J. González
  • H. Pomares
  • L. J. Herrera
  • B. Paechter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4304)

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.

Keywords

Migration Rate Pareto Front Mutation Operator Crossover Operator Radial Basis Function Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • A. Guillén
    • 1
  • I. Rojas
    • 1
  • J. González
    • 1
  • H. Pomares
    • 1
  • L. J. Herrera
    • 1
  • B. Paechter
    • 2
  1. 1.Department of Computer Architecture and Computer TechnologyUniversidad de GranadaSpain
  2. 2.School of ComputingNapier UniversityScotland

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