The impact of population size on code growth in GP: Analysis and empirical validation

Riccardo Poli, Nicholas Freitag McPhee, Leonardo Vanneschi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Scopus citations

Abstract

The crossover bias theory for bloat [18] is a recent result which predicts that bloat is caused by the sampling of short, unfit programs. This theory is clear and simple, but it has some weaknesses: (1) it implicitly assumes that the population is large enough to allow sampling of all relevant program sizes (although it does explain what to expect in the many practical cases where this is not true, e.g., because the population is small); (2) it does not explain what is meant by its assumption that short programs are unfit. In this paper we discuss these weaknesses and propose a refined version of the crossover bias theory that clarifies the relationship between bloat and finite populations, and explains what features of the fitness landscape cause bloat to occur. The theory, in particular, predicts that smaller populations will bloat more slowly than larger ones. Additionally, the theory predicts that bloat will only be observed in problems where short programs are less fit than longer ones when looking at samples created by fitness-based importance sampling, i.e. samplings of the search space in which fitter programs have a higher probability of being sampled (e.g., the Metropolis-Hastings method). Experiments with two classical GP benchmarks fully corroborate the theory.

Original languageEnglish (US)
Title of host publicationGECCO'08
Subtitle of host publicationProceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008
Pages1275-1282
Number of pages8
StatePublished - Dec 15 2008
Event10th Annual Genetic and Evolutionary Computation Conference, GECCO 2008 - Atlanta, GA, United States
Duration: Jul 12 2008Jul 16 2008

Publication series

NameGECCO'08: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation 2008

Other

Other10th Annual Genetic and Evolutionary Computation Conference, GECCO 2008
Country/TerritoryUnited States
CityAtlanta, GA
Period7/12/087/16/08

Keywords

  • Bloat
  • Genetic programming
  • Population size

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