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Genetic and Evolutionary Computation — GECCO 2003

Volume 2724 of the series Lecture Notes in Computer Science pp 1332-1344

Date:

Scalability of Selectorecombinative Genetic Algorithms for Problems with Tight Linkage

  • Kumara SastryAffiliated withIllinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-ChampaignDepartment of Material Science & Engineering, University of Illinois at Urbana-Champaign
  • , David E. GoldbergAffiliated withIllinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-ChampaignDepartment of General Engineering, University of Illinois at Urbana-Champaign

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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 (2k m 1.5), where k is the BB size, and m is the number of BBs.