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Maintaining Population Diversity in Evolutionary Art

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Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7247)

Abstract

Evolutionary art is inherently more concerned with exploration than with exploitation, because users are typically more interested in evolving a collection of diverse images than converging to a single ‘optimal’ image. However, maintaining diversity is a difficult task. In this paper we investigate various techniques to promote population diversity in evolutionary art. We introduce customised mutation and crossover operators that perform a local search to diversify individuals and evaluate the effect of these operators on population diversity. We also investigate alternatives for the fitness crowding operator in NSGA-II; we use a genotype and a phenotype distance function to calculate the crowding distance and investigate their effect on population diversity.

Keywords

  • Local Search
  • Distance Function
  • Genetic Program
  • Pareto Front
  • Population Diversity

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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den Heijer, E., Eiben, A.E. (2012). Maintaining Population Diversity in Evolutionary Art. In: Machado, P., Romero, J., Carballal, A. (eds) Evolutionary and Biologically Inspired Music, Sound, Art and Design. EvoMUSART 2012. Lecture Notes in Computer Science, vol 7247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29142-5_6

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  • DOI: https://doi.org/10.1007/978-3-642-29142-5_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29141-8

  • Online ISBN: 978-3-642-29142-5

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