Optimal Sampling and Speed-Up for Genetic Algorithms on the Sampled OneMax Problem
نویسندگان
چکیده
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 sampling makes no difference for the gambler’s ruin model. The speed-up obtained by sampling is then empirically verified.
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