Computer Language
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Nelder-Mead simplex modifications for simulation optimization
Management Science
`` Direct Search'' Solution of Numerical and Statistical Problems
Journal of the ACM (JACM)
Direct search methods: then and now
Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. IV: optimization and nonlinear equations
Remark on algorithm 178 [E4] direct search
Communications of the ACM
The theory of evolution strategies
The theory of evolution strategies
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Noisy Local Optimization with Evolution Strategies
Noisy Local Optimization with Evolution Strategies
A Direct Search Algorithm for Optimization with Noisy Function Evaluations
SIAM Journal on Optimization
Convergence of the Nelder--Mead Simplex Method to a Nonstationary Point
SIAM Journal on Optimization
SIAM Journal on Optimization
Genetic Algorithms in Noisy Environments
Machine Learning
Global Optimization by Means of Distributed Evolution Strategies
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
A Revised Simplex Search Procedure for Stochastic Simulation Response Surface Optimization
INFORMS Journal on Computing
Multidirectional search: a direct search algorithm for parallel machines
Multidirectional search: a direct search algorithm for parallel machines
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Analysis of the (μ/μ, λ) - ES on the Parabolic Ridge
Evolutionary Computation
Toward a theory of evolution strategies: On the benefits of sex---the (μ/μ, λ) theory
Evolutionary Computation
Toward a theory of evolution strategies: Self-adaptation
Evolutionary Computation
Genetic algorithms, selection schemes, and the varying effects of noise
Evolutionary Computation
Evolutionary computation: comments on the history and current state
IEEE Transactions on Evolutionary Computation
Local convergence rates of simple evolutionary algorithms withCauchy mutations
IEEE Transactions on Evolutionary Computation
Evolutionary algorithms and gradient search: similarities anddifferences
IEEE Transactions on Evolutionary Computation
On the robustness of population-based versus point-basedoptimization in the presence of noise
IEEE Transactions on Evolutionary Computation
Local performance of the (1 + 1)-ES in a noisy environment
IEEE Transactions on Evolutionary Computation
A New Approach for Predicting the Final Outcome of Evolution Strategy Optimization Under Noise
Genetic Programming and Evolvable Machines
Enhancing evolutionary algorithms with statistical selection procedures for simulation optimization
WSC '05 Proceedings of the 37th conference on Winter simulation
Simulation optimization using tabu search: an emperical study
WSC '05 Proceedings of the 37th conference on Winter simulation
Weighted multirecombination evolution strategies
Theoretical Computer Science - Foundations of genetic algorithms
Aiming for a theoretically tractable CSA variant by means of empirical investigations
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Embedded evolutionary multi-objective optimization for worst case robustness
Proceedings of the 10th annual conference on Genetic and evolutionary computation
On Multiplicative Noise Models for Stochastic Search
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Evolution strategies with cumulative step length adaptation on the noisy parabolic ridge
Natural Computing: an international journal
An evolutionary method for complex-process optimization
Computers and Operations Research
The steady state behavior of (µ/µI, λ)-ES on ellipsoidal fitness models disturbed by noise
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Selection in the presence of noise
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Hybrid numerical optimization for combinatorial network problems
HM'07 Proceedings of the 4th international conference on Hybrid metaheuristics
A differential evolution for optimisation in noisy environment
International Journal of Bio-Inspired Computation
A preliminary study on handling uncertainty in indicator-based multiobjective optimization
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Integrating techniques from statistical ranking into evolutionary algorithms
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Optimal weighted recombination
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
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Evolution strategies are general, nature-inspired heuristics for search and optimization. Due to their use of populations of candidate solutions and their advanced adaptation schemes, there is a common belief that evolution strategies are especially useful for optimization in the presence of noise. Empirical evidence as well as a number of theoretical findings with respect to the performance of evolution strategies on a class of spherical objective functions disturbed by Gaussian noise support that belief. However, little is known with respect to the capabilities in the presence of noise of evolution strategies relative to those of other direct optimization strategies.In the present paper, theoretical results with respect to the performance of evolution strategies in the presence of Gaussian noise are summarized and discussed. Then, the performance of evolution strategies is compared empirically with that of several other direct optimization strategies in the noisy, spherical environment that the theoretical results have been obtained in. Due to the simplicity of that environment, the results are easily interpretable and can serve to reveal the respective strengths and weaknesses of the algorithms. It is seen that for low levels of noise, most of the strategies exhibit similar degrees of efficiency. For higher levels of noise, their step length adaptation scheme affords evolution strategies a greater degree of robustness than the other algorithms tested.