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Memetic Crossover for Genetic Programming: Evolution Through Imitation

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Genetic and Evolutionary Computation – GECCO 2004 (GECCO 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3103))

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Abstract

For problems where the evaluation of an individual is the dominant factor in the total computation time of the evolutionary process, minimizing the number of evaluations becomes critical. This paper introduces a new crossover operator for genetic programming, memetic crossover, that reduces the number of evaluations required to find an ideal solution. Memetic crossover selects individuals and crossover points by evaluating the observed strengths and weaknesses within areas of the problem. An individual that has done poorly in some parts of the problem may then imitate an individual that did well on those same parts. This results in an intelligent search of the feature-space and, therefore, fewer evaluations.

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Author information

Authors and Affiliations

  1. University of Oklahoma, Norman, OK, 73019, USA

    Brent E. Eskridge & Dean F. Hougen

Authors
  1. Brent E. Eskridge
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  2. Dean F. Hougen
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Editor information

Editors and Affiliations

  1. Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), Kanpur, 208016, Uttar Pradesh, India

    Kalyanmoy Deb

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Eskridge, B.E., Hougen, D.F. (2004). Memetic Crossover for Genetic Programming: Evolution Through Imitation. In: Deb, K. (eds) Genetic and Evolutionary Computation – GECCO 2004. GECCO 2004. Lecture Notes in Computer Science, vol 3103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24855-2_57

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  • DOI: https://doi.org/10.1007/978-3-540-24855-2_57

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22343-6

  • Online ISBN: 978-3-540-24855-2

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