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Optimal Sampling and Speed-Up for Genetic Algorithms on the Sampled OneMax Problem
Tian-Li Yu1, David E. Goldberg2, and Kumara Sastry3
Illinois Genetic Algorithms Laboratory (IlliGAL)
Department of General Engineering
University of Illinois at Urbana-Champaign
104 S. Mathews Ave.
Urbana, IL 61801
{tianliyu,deg,kumara}@illigal.ge.uiuc.edu
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.
LNCS 2724, p. 1554 ff.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2003