Optimal sampling and speed-up for genetic algorithms on the sampled OneMax problem

Authors:
Tian-Li Yu;David E. Goldberg;Kumara Sastry
Affiliations:
Illinois Genetic Algorithms Laboratory, Department of General Engineering, University of Illinois at Urbana-Champaign, Urbana, IL;Illinois Genetic Algorithms Laboratory, Department of General Engineering, University of Illinois at Urbana-Champaign, Urbana, IL;Illinois Genetic Algorithms Laboratory, 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

This paper investigates the optimal sampling and the speed-up obtained through sampling for the sampled OneMax problem. Theoretical and experimental analyses are given for three different population-sizing models: the decision-making model, the gambler's ruin model, and the fixed population-sizing model. The results suggest that, when the desired solution quality is fixed to a high value, the decision-making model prefers a large sampling size, the fixed population-sizing model prefers a small sampling size, and the gambler's ruin model has no preference for large or small sizes. Among the three population-sizing models, sampling yields speed-up only when the fixed population-sizing model is valid. The results indicate that when the population is sized appropriately, sampling does not yield speed-up for problems with subsolutions of uniform salience.