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Selective breeding in a multiobjective genetic algorithm

  • G. T. Parks
  • I. Miller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1498)

Abstract

This paper describes an investigation of the efficacy of various elitist selection strategies in a multiobjective Genetic Algorithm implementation, with parents being selected both from the current population and from the archive record of nondominated solutions encountered during search. It is concluded that, because the multiobjective optimization process naturally maintains diversity in the population, it is possible to improve the performance of the algorithm through the use of strong elitism and high selection pressures without suffering the disadvantages of genetic convergence which such strategies would bring in single objective optimization.

Keywords

Pareto Front Multiobjective Optimization Fuel Assembly Nondominated Solution Multiobjective Genetic Algorithm 
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 1998

Authors and Affiliations

  • G. T. Parks
    • 1
  • I. Miller
    • 1
  1. 1.Engineering Design CentreCambridge University Engineering DepartmentCambridgeUK

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