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.
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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
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