Stopping criteria for genetic algorithms with application to multiobjective optimization
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Study of multiscale global optimization based on parameter space partition
Journal of Global Optimization
An analysis of the solution quality of the simple genetic algorithm
BICA'12 Proceedings of the 5th WSEAS congress on Applied Computing conference, and Proceedings of the 1st international conference on Biologically Inspired Computation
A network theoretic analysis of evolutionary algorithms
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Variance as a Stopping Criterion for Genetic Algorithms with Elitist Model
Fundamenta Informaticae
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In this paper we discuss convergence properties for genetic algorithms. By looking at the effect of mutation on convergence, we show that by running the genetic algorithm for a sufficiently long time we can guarantee convergence to a global optimum with any specified level of confidence. We obtain an upper bound for the number of iterations necessary to ensure this, which improves previous results. Our upper bound decreases as the population size increases. We produce examples to show that in some cases this upper bound is asymptotically optimal for large population sizes. The final section discusses implications of these results for optimal coding of genetic algorithms.