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Genetic Programming — Introduction, Applications, Theory and Open Issues

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Abstract

Genetic programming (GP) is an evolutionary approach that extends genetic algorithms to allow the exploration of the space of computer programs. Like other evolutionary algorithms, GP works by defining a goal in the form of a quality criterion (or fitness) and then using this criterion to evolve a set (or population) of candidate solutions (individuals) by mimicking the basic principles of Darwinian evolution. GP breeds the solutions to problems using an iterative process involving the probabilistic selection of the fittest solutions and their variation by means of a set of genetic operators, usually crossover and mutation. GP has been successfully applied to a number of challenging real-world problem domains. Its operations and behavior are now reasonably well understood thanks to a variety of powerful theoretical results. In this chapter, we introduce the main definitions and features of GP and describe its typical operations. We then survey some of its applications. We also review some important theoretical results in this field, including some very recent ones, and discuss some of the most challenging open issues and directions for future research.

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Author information

Authors and Affiliations

  1. Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

    Leonardo Vanneschi

  2. Department of Computing and Electronic Systems, University of Essex, Colchester, UK

    Riccardo Poli

Authors
  1. Leonardo Vanneschi
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  2. Riccardo Poli
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Corresponding authors

Correspondence to Leonardo Vanneschi or Riccardo Poli .

Editor information

Editors and Affiliations

  1. LIACS, Leiden University, Leiden, The Netherlands

    Grzegorz Rozenberg

  2. Computer Science Department, University of Colorado, Boulder, USA

    Grzegorz Rozenberg

  3. LIACS, Leiden University, Leiden, The Netherlands

    Thomas Bäck

  4. LIACS, Leiden University, Leiden, The Netherlands

    Joost N. Kok

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Vanneschi, L., Poli, R. (2012). Genetic Programming — Introduction, Applications, Theory and Open Issues. In: Rozenberg, G., Bäck, T., Kok, J.N. (eds) Handbook of Natural Computing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92910-9_24

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