Scalability of selectorecombinative genetic algorithms for problems with tight linkage

Authors:
Kumara Sastry;David E. Goldberg
Affiliations:
Illinois Genetic Algorithms Laboratory and Department of Material Science & Engineering, University of Illinois at Urbana-Champaign, Urbana, IL;Illinois Genetic Algorithms Laboratory and Department of General Engineering, University of Illinois at Urbana-Champaign, Urbana, IL
Venue:
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Year:
2003

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Abstract

Ensuring building-block (BB) mixing is critical to the success of genetic and evolutionary algorithms. This study develops facetwise models to predict the BB mixing time and the population sizing dictated by BB mixing for single-point crossover. The population-sizing model suggests that for moderate-to-large problems, BB mixing - instead of BB decision making and BB supply - bounds the population size required to obtain a solution of constant quality. Furthermore, the population sizing for single-point crossover scales as O (2km1.5), where k is the BB size, and m is the number of BBs.