Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
A Comparison of Evolution Strategies with Other Direct Search Methods in the Presence of Noise
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On the benefits of populations for noisy optimization
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A Cultural Algorithm for POMDPs from Stochastic Inventory Control
HM '08 Proceedings of the 5th International Workshop on Hybrid Metaheuristics
Weighted recombination evolution strategy on a class of PDQF's
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Evolutionary multi-objective quantum control experiments with the covariance matrix adaptation
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Handling uncertainties in evolutionary multi-objective optimization
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
Active covariance matrix adaptation for the (1+1)-CMA-ES
Proceedings of the 12th annual conference on Genetic and evolutionary computation
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MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Quantum control experiments as a testbed for evolutionary multi-objective algorithms
Genetic Programming and Evolvable Machines
Noisy optimization complexity under locality assumption
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
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While noise is a phenomenon present in many real world optimization problems, the understanding of its potential effects on the performance of evolutionary algorithms is still incomplete. This paper investigates the effects of fitness proportionate Gaussian noise for a (1 + 1)-ES with isotropic normal mutations on the quadratic sphere in the limit of infinite search-space dimensionality. It is demonstrated experimentally that the results provide a good approximation for finite space dimensionality. It is shown that overvaluation as a result of failure to re-evaluate parental fitness leads to both reduced success probabilities and improved performance. Implications for mutation strength adaptation rules are discussed and optimal re-sampling rates are computed