Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Genetic Algorithms in Noisy Environments
Machine Learning
Averaging Efficiently in the Presence of Noise
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Evolution Strategies on Noisy Functions: How to Improve Convergence Properties
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Toward a theory of evolution strategies: Some asymptotical results from the (1,+ λ)-theory
Evolutionary Computation
Toward a theory of evolution strategies: On the benefits of sex---the (μ/μ, λ) theory
Evolutionary Computation
On Multiplicative Noise Models for Stochastic Search
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Bandit-based estimation of distribution algorithms for noisy optimization: rigorous runtime analysis
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
Handling expensive optimization with large noise
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Noisy optimization complexity under locality assumption
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
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Noise is present in many optimization problems. Evolutionary algorithms are frequently reported to be robust with regard to the effects of noise. In many cases, there is a tradeoff between the accuracy with which the fitness of a candidate solution is determined and the number of candidate solutions that are evaluated in every time step. This paper addresses this tradeoff on the basis of recently established results from the analysis of the local performance of a recombinant multiparent evolution strategy on a noisy sphere. It is shown that, provided that mutation strengths are appropriately adapted, the strategy is indeed able to cope with noise, and that results previously obtained for single-parent evolution strategies do not carry over to multi-parent strategies. Then, the problem of mutation strength adaptation in noisy environments is addressed. Mutative self-adaptation and the cumulative mutation strength adaptation algorithm are compared empirically in a simple fitness environment. The results suggest that both algorithms are prone to failure in the presence of noise.