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Evolutionary Games

  • Reference work entry
  • First Online:
Encyclopedia of Machine Learning and Data Mining

  • 160 Accesses

Definition

Evolutionary algorithms are a family of algorithms inspired by the workings of evolution by natural selection, whose basic structure is to:

  1. 1.

    Produce an initial population of individuals, these latter being candidate solutions to the problem at hand.

  2. 2.

    Evaluate the fitness of each individual in accordance with the problem whose solution is sought.

  3. 3.

    While termination condition not met do:

    1. (a)

      Select fitter individuals for reproduction

    2. (b)

      Recombine (crossover) individuals

    3. (c)

      Mutate individuals

    4. (d)

      Evaluate fitness of modified individuals

  4. 4.

    End while

Evolutionary games is the application of evolutionary algorithms to the evolution of game-playing strategies for various games, including chess, backgammon, and Robocode.

Motivation and Background

Ever since the dawn of artificial intelligence in the 1950s, games have been part and parcel of this lively field. In 1957, a year after the Dartmouth Conference that marked the official birth of AI, Alex Bernstein designed a...

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Recommended Reading

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Author information

Authors and Affiliations

  1. Ben-Gurion University, Beer-Sheva, Israel

    Moshe Sipper

Authors
  1. Moshe Sipper
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Corresponding author

Correspondence to Moshe Sipper .

Editor information

Editors and Affiliations

  1. The University of New South Wales, Sydney, NSW, Australia

    Claude Sammut

  2. Faculty of Information Technology, Monash University, Melbourne, VIC, Australia

    Geoffrey I. Webb

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Sipper, M. (2017). Evolutionary Games. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_92

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