期刊
INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS
卷 79, 期 4, 页码 403-416出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207160210939
关键词
genetic algorithms performance; generational replacement model; steady-state replacement model; population size; pseudo-random number generators; ISAAC PNG
This paper presents a review and experimental results oil the major benchmarking functions used for performance control of Genetic Algorithms (GAs). Parameters considered include the eect of population size, crossover probability and pseudo-random number generators (PNGs). The general computational behavior of two basic GAs models, the Generational Replacement Model (GRM) and the Steady State Replacement Model (SSRM) is evaluated.
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