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Introducing Multi-objective Optimization in Cooperative Coevolution of Neural Networks

  • N. García-Pedrajas
  • E. Sanz-Tapia
  • D. Ortiz-Boyer
  • C. Hervás-Martínez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2084)

Abstract

This paper presents MONet (Multi-Objective coevolutive NETwork), a cooperative coevolutionary model for evolving artificial neural networks that introduces concepts taken from multi-objective optimization. This model is based on the idea of coevolving subnetworks that must cooperate to form a solution for a specific problem, instead of evolving complete networks. The fitness of each member of the subpopulations of subnetworks is evaluated using an evolutionary multi-objective optimization algorithm. This idea has not been used before in the area of evolutionary artificial neural networks. The use of a multiobjective evolutionary algorithm allows the definition of as many objectives as could be interesting for our problem and the optimization of these objectives in a natural way.

Keywords

Genetic Algorithm Multiobjective Optimization Multiobjective Evolutionary Algorithm Strength Pareto Evolutionary Algorithm Cooperative Coevolution 
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 2001

Authors and Affiliations

  • N. García-Pedrajas
    • 1
  • E. Sanz-Tapia
    • 1
  • D. Ortiz-Boyer
    • 1
  • C. Hervás-Martínez
    • 1
  1. 1.Dept. of Computing and Numerical AnalysisUniversity of CórdobaCórdobaSpain

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