Genetic Algorithms in Noisy Environments
Machine Learning
Efficiency and Mutation Strength Adaptation of the (mu, muI, lambda)-ES in a Noisy Environment
PPSN VI Proceedings of the 6th 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
Interfaces - Special issue: Franz Edelman award for achievement in operations research and the management sciences
Using confidence bounds for exploitation-exploration trade-offs
The Journal of Machine Learning Research
Proceedings of the 25th international conference on Machine learning
On Multiplicative Noise Models for Stochastic Search
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Hoeffding and Bernstein races for selecting policies in evolutionary direct policy search
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
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
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In this paper, adaptive noisy optimization on variants of the noisy sphere model is considered, i.e. optimization in which the same algorithm is able to adapt to several frameworks, including some for which no bound has never been derived. Incidentally, bounds derived by [16] for noise quickly decreasing to zero around the optimum are extended to the more general case of a positively lower-bounded noise thanks to a careful use of Bernstein bounds (using empirical estimates of the variance) instead of Chernoff-like variants.