The Racing Algorithm: Model Selection for Lazy Learners
Artificial Intelligence Review - Special issue on lazy learning
The theory of evolution strategies
The theory of evolution strategies
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Finite-time Analysis of the Multiarmed Bandit Problem
Machine Learning
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
Theoretical Computer Science
Convergence results for the (1, λ)-SA-ES using the theory of ϕ-irreducible Markov chains
Theoretical Computer Science
Comparison-based algorithms are robust and randomized algorithms are anytime
Evolutionary Computation
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
Cumulative step length adaptation for evolution strategies using negative recombination weights
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
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
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
Approximating the least hypervolume contributor: NP-hard in general, but fast in practice
Theoretical Computer Science
Noisy optimization complexity under locality assumption
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
Noisy optimization convergence rates
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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We show complexity bounds for noisy optimization, in frameworks in which noise is stronger than in previously published papers[19]. We also propose an algorithm based on bandits (variants of [16]) that reaches the bound within logarithmic factors. We emphasize the differences with empirical derived published algorithms. Complete mathematical proofs can be found in [26].