Tournament versus Fitness Uniform Selection

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In evolutionary algorithms a critical parameter that must be tuned is that of selection pressure. If it is set too low then the rate of convergence towards the optimum is likely to be slow. Alternatively if the selection pressure is set too high the system is likely to become stuck in a local optimum due to a loss of diversity in the population. The recent Fitness Uniform Selection Scheme (FUSS) is a conceptually simple but somewhat radical approach to addressing this problem - rather than biasing the selection towards higher fitness, FUSS biases selection towards sparsely populated fitness levels. In this paper we compare the relative performance of FUSS with the well known tournament selection scheme on a range of problems.
10 pages, 8 figures

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Machine Learning, Artificial Intelligence, I.2; I.2.6; I.2.8; F.2

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http://salesiana.dossiersoluciones.com/handle/123456789/32052

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/cs/0403038
 http://arxiv.org/abs/cs/0403038
 Proc. 2004 Congress on Evolutionary Computation (CEC-2004), pages 2144--2151

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arXiv