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On benchmarking functions for genetic algorithms. (English) Zbl 0984.65004

Summary: This paper presents experimental results on the major benchmarking functions used for performance evaluation of genetic algorithms (GAs). Parameters considered included the effect of population size, crossover probability, mutation rate and pseudorandom generator. The general computational behavior of two basic GAs models, the generational replacement model and the steady state replacement model is evaluated.

MSC:

65C10 Random number generation in numerical analysis
92D10 Genetics and epigenetics

Software:

GEATbx; PGAPack
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References:

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