The theory of evolution strategies
The theory of evolution strategies
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Step-Size Adaption Based on Non-Local Use of Selection Information
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Weighted multirecombination evolution strategies
Theoretical Computer Science - Foundations of genetic algorithms
On the use of evolution strategies for optimising certain positive definite quadratic forms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Toward a theory of evolution strategies: Self-adaptation
Evolutionary Computation
Step length adaptation on ridge functions
Evolutionary Computation
Mutative self-adaptation on the sharp and parabolic ridge
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
Rigorous runtime analysis of the (1+1) ES: 1/5-rule and ellipsoidal fitness landscapes
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
Noisy optimization: a theoretical strategy comparison of ES, EGS, SPSA & IF on the noisy sphere
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Theoretical Computer Science
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This paper investigates the behavior of (µ/µI, λ)- σSA-ES on a class of positive definite quadratic forms. After introducing the fitness environment and the strategy, the self-adaptation mechanism is analyzed with the help of the self-adaptation response function. Afterward, the steady state of the strategy is analyzed. The dynamical equations for the expectation of the mutation strength σ and the localization parameter ζ will be derived. Building on that, the progress rate ϕ is analyzed and tuned by means of the learning parameter τ. An approximate formula for τopt, yielding locally maximal progress, is presented. Finally, the performance of the σSA-rule is compared with the performance of the cumulative step size adaptation rule, and a rough approximation for the expected runtime is presented.