On benchmarking functions for genetic algorithms

Abstract

This paper presents experimental results on the major benchmarking functions used for performance evaluation of Genetic Algorithms (GAs). Parameters considered include 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 (GRM) and the Steady State Replacement Model (SSRM) is evaluated.

Keywords:

• Genetic algorithms
• Benchmarking functions
• Population size
• Mutation rate
• Pseudo-random number generation

C.R. Categories::

• I.2.8
• F.2.2

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Notes

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