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The Genetic Algorithm

  • Miroslav Kubat
Chapter

Abstract

The essence of machine learning is the search for the best solution to our problem: to find a classifier which classifies as correctly as possible not only the training examples, but also future examples. Chapter  1 explained the principle of one of the most popular AI-based search techniques, the so-called hill-climbing, and showed how it can be used in classifier induction.

References

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    Fogel, L. J., Owens, A. J., & Walsh, M. J. (1966). Artificial intelligence through simulated evolution. New York: Wiley.Google Scholar
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    Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor: University of Michigan Press.Google Scholar
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    Rechenberg, I. (1973). Evolutionsstrategie: Optimierung technischer Systeme nach Principien der biologischen Evolution. Stuttgart: Frommann-Holzboog.Google Scholar
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    Rozsypal, A. & Kubat, M. (2001). Using the genetic algorithm to reduce the size of a nearest-neighbor classifier and to select relevant attributes. In Proceedings of the 18th international conference on machine learning, Williamstown (pp. 449–456).Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Miroslav Kubat
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of MiamiCoral GablesUSA

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