Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on uniform random number generation
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Calculating the expected loss of diversity of selection schemes
Evolutionary Computation
Optimization and assessment of wavelet packet decompositions with evolutionary computation
EURASIP Journal on Applied Signal Processing
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Viewing the selection process in a genetic algorithm as a two-step procedure consisting of the assignment of selection probabilities and the sampling according to this distribution, we employ the χ2 measure as a tool for the analysis of the stochastic properties of the sampling. We are thereby able to compare different selection schemes even in the case that their probability distributions coincide. Introducing a new sampling algorithm with adjustable accuracy and employing two-level test designs enables us to further reveal the intrinsic correlation structures of well-known sampling algorithms. Our methods apply well to integral methods like tournament selection and can be automated.