Bad subsequences of well-known linear congruential pseudorandom number generators
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Asynchronous Genetic Algorithms on Parallel Computers
Proceedings of the 5th International Conference on Genetic Algorithms
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The impact of the random sequence on Genetic Algorithms (GAs) is rarely discussed in the community so far. The requirements of GAs for Pseudo Random Number Generators (PRNGs) are analyzed, and a series of numerical experiments of Genetic Algorithm and Direct Search Toolbox computing three different kinds of typical test functions are conducted.An estimate of solution accuracy for each test function is included when six standard PRNGs on MATLAB are applied respectively. A ranking is attempted based on the estimated solution absolute/relative error. It concludes that the effect of PRNGs on GAs varies with the test function; that generally speaking, modern PRNGs outperform traditional ones, and that the seed also has a deep impact on GAs. The research results will be beneficial to stipulate proper principle of PRNGs selection criteria for GAs.