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Is Self-adaptation of Selection Pressure and Population Size Possible? – A Case Study

  • A. E. Eiben
  • M. C. Schut
  • A. R. de Wilde
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4193)

Abstract

In this paper we seek an answer to the following question: Is it possible and rewarding to self-adapt parameters regarding selection and population size in an evolutionary algorithm? The motivation comes from the observation that the majority of the existing EC literature is concerned with (self-)adaptation of variation operators, while there are indications that (self-)adapting selection operators or the population size can be equally or even more rewarding. We approach the question in an empirical manner. We design and execute experiments for comparing the performance increase of a benchmark EA when augmented with self-adaptive control of parameters concerning selection and population size in isolation and in combination. With the necessary caveats regarding the test suite and the particular mechanisms used we observe that self-adapting selection yields the highest benefit (up to 30-40%) in terms of speed.

Keywords

Population Size Evolutionary Algorithm Selection Pressure Global Parameter Tournament Size 
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. E. Eiben
    • 1
  • M. C. Schut
    • 1
  • A. R. de Wilde
    • 1
  1. 1.Department of Computer ScienceVrije Universiteit Amsterdam 

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