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Advanced Course on Artificial Intelligence

ACAI 1999: Machine Learning and Its Applications pp 146–168Cite as

Genetic Algorithms in Machine Learning

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2049))

Abstract

Genetic algorithms are stochastic search algorithms which act on a population of possible solutions. They are loosely based on the mechanics of population genetics and selection. The potential solutions are encoded as ‘genes’ — strings of characters from some alphabet. New solutions can be produced by ‘mutating’ members of the current population, and by ‘mating’ two solutions together to form a new solution. The better solutions are selected to breed and mutate and the worse ones are discarded. They are probabilistic search methods; this means that the states which they explore are not determined solely by the properties of the problems. A random process helps to guide the search. Genetic algorithms are used in artificial intelligence like other search algorithms are used in artificial intelligence — to search a space of potential solutions to find one which solves the problem.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Manchester, Manchester, M13 9PL, UK

    Jonathan Shapiro

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  1. Jonathan Shapiro
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Editors and Affiliations

  1. National Centre for Scientific Research “Demokritos”, Institute of Informatics and Telecommunications, P.O. Box 60228, Ag. Paraskevi, 15310, Athens, Greece

    Georgios Paliouras , Vangelis Karkaletsis  & Constantine D. Spyropoulos ,  & 

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© 2001 Springer-Verlag Berlin Heidelberg

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Shapiro, J. (2001). Genetic Algorithms in Machine Learning. In: Paliouras, G., Karkaletsis, V., Spyropoulos, C.D. (eds) Machine Learning and Its Applications. ACAI 1999. Lecture Notes in Computer Science(), vol 2049. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44673-7_7

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  • DOI: https://doi.org/10.1007/3-540-44673-7_7

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42490-1

  • Online ISBN: 978-3-540-44673-6

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