The theory of evolution strategies
The theory of evolution strategies
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Noisy Local Optimization with Evolution Strategies
Noisy Local Optimization with Evolution Strategies
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Searching in the Presence of Noise
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
A New Approach for Predicting the Final Outcome of Evolution Strategy Optimization Under Noise
Genetic Programming and Evolvable Machines
Self-adaptation of evolution strategies under noisy fitness evaluations
Genetic Programming and Evolvable Machines
A derandomized approach to self-adaptation of evolution strategies
Evolutionary Computation
Toward a theory of evolution strategies: Self-adaptation
Evolutionary Computation
Evolution strategies with cumulative step length adaptation on the noisy parabolic ridge
Natural Computing: an international journal
Mutative self-adaptation on the sharp and parabolic ridge
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
Cumulative step length adaptation on ridge functions
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Searching for balance: understanding self-adaptation on ridge functions
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
On the performance of (1, λ)-evolution strategies for theridge function class
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
On the Behaviour of the (1+1)-ES for a Simple Constrained Problem
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
On the limitations of adaptive resampling in using the student's t-test evolution strategies
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Using the uncertainty handling CMA-ES for finding robust optima
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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This paper considers self-adaptive (mu/mu_I,lambda)-evolution strategies on the noisy sharp ridge. The evolution strategy (ES) is treated as a dynamical system using the so-called evolution equations to model the ES's behavior. The approach requires the determination of the one-generational expected changes of the state variables - the progress measures. For the analysis, the stationary state behavior of the ES on the sharp ridge is considered. Contrary to the usual perception of noise, it is shown that noise has a positive influence on the performance. An explanation for this astonishing behavior is given and conditions for the usefulness of noise in other fitness landscapes are discussed.