Optimal Sampling and Speed-Up for Genetic Algorithms on the Sampled OneMax Problem

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.

[1]  Brad L. Miller,et al.  Noise, sampling, and efficient genetic algorthms , 1997 .

[2]  David E. Goldberg,et al.  Optimal Sampling For Genetic Algorithms , 1996 .

[3]  Zbigniew Michalewicz,et al.  Evolutionary Computation 1 , 2018 .

[4]  Kalyanmoy Deb,et al.  Genetic Algorithms, Noise, and the Sizing of Populations , 1992, Complex Syst..

[5]  David E. Goldberg,et al.  Genetic Algorithms, Selection Schemes, and the Varying Effects of Noise , 1996, Evolutionary Computation.

[6]  David E. Goldberg,et al.  The Race, the Hurdle, and the Sweet Spot , 1998 .

[7]  Dirk Thierens,et al.  Convergence Models of Genetic Algorithm Selection Schemes , 1994, PPSN.

[8]  John J. Grefenstette,et al.  Genetic Search with Approximate Function Evaluation , 1985, ICGA.

[9]  Philippe Giguere.  Population Sizing for Optimum Sampling with Genetic Algorithms: A Case Study of the Onemax Problem , 1998 .

[10]  David E. Goldberg,et al.  Genetic Algorithms, Efficiency Enhancement, And Deciding Well With Differing Fitness Variances , 2002, GECCO.

[11]  T. Back.  Selective pressure in evolutionary algorithms: a characterization of selection mechanisms , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[12]  E. Cantu-Paz,et al.  The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations , 1997, Evolutionary Computation.

[13]  Heinz Mühlenbein,et al.  Predictive Models for the Breeder Genetic Algorithm I. Continuous Parameter Optimization , 1993, Evolutionary Computation.