The theory of evolution strategies
The theory of evolution strategies
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
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
Convergence results for the (1, λ)-SA-ES using the theory of ϕ-irreducible Markov chains
Theoretical Computer Science
On Multiplicative Noise Models for Stochastic Search
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Uncertainty handling CMA-ES for reinforcement learning
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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
Lower Bounds for Comparison Based Evolution Strategies Using VC-dimension and Sign Patterns
Algorithmica - Special Issue: Theory of Evolutionary Computation
Handling expensive optimization with large noise
Proceedings of the 11th workshop proceedings on Foundations of genetic algorithms
Towards a complexity theory of randomized search heuristics: ranking-based black-box complexity
CSR'11 Proceedings of the 6th international conference on Computer science: theory and applications
General lower bounds for evolutionary algorithms
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Local performance of the (1 + 1)-ES in a noisy environment
IEEE Transactions on Evolutionary Computation
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In spite of various recent publications on the subject, there are still gaps between upper and lower bounds in evolutionary optimization for noisy objective function. In this paper we reduce the gap, and get tight bounds within logarithmic factors in the case of small noise and no long-distance influence on the objective function.