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Genetic Algorithms for the Generation of Models with Micropopulations

Part of the Lecture Notes in Computer Science book series (LNCS,volume 2611)

Abstract

The present article puts forward a method for an interactive model generation through the use of Genetic Algorithms applied to small populations. Micropopulations actually worsen the problem of the premature convergence of the algorithm, since genetic diversity is very limited. In addition, some key factors, which modify the changing likelihood of alleles, cause the likelihood of premature convergence to decrease. The present technique has been applied to the design of 3D models, starting from generic and standard pieces, using objective searches and searches with no defined objective.

Keywords

  • Genetic Algorithm
  • Mutation Operator
  • Premature Convergence
  • Mutation Factor
  • Objective Search

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Sáez, Y., Sanjuán, O., Segovia, J., Isasi, P. (2003). Genetic Algorithms for the Generation of Models with Micropopulations. In: , et al. Applications of Evolutionary Computing. EvoWorkshops 2003. Lecture Notes in Computer Science, vol 2611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36605-9_52

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  • DOI: https://doi.org/10.1007/3-540-36605-9_52

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00976-4

  • Online ISBN: 978-3-540-36605-8

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