A Genetic Algorithm-specific Test Of Random Generator Quality
نویسندگان
چکیده
It has been shown in past research that pseudo-random number generator (PRNG) quality can impact the performance of simple genetic algorithms (GAs). However, standard empirical tests of random generator quality are not good predictors of when such impacts are likely to occur. In this paper, we introduce a new test of random generator quality, tailored to specific instances of a simple GA. This test has been shown to be a better predictor of GA performance impacts than standard empirical tests. 1 PRNG QUALITY AND GA
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