The theory of evolution strategies
The theory of evolution strategies
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
On the analysis of the (1+ 1) evolutionary algorithm
Theoretical Computer Science
A Comparison of Evolution Strategies with Other Direct Search Methods in the Presence of Noise
Computational Optimization and Applications
Qualms regarding the optimality of cumulative path length control in CSA/CMA-evolution strategies
Evolutionary Computation
How the (1 + 1) ES using isotropic mutations minimizes positive definite quadratic forms
Theoretical Computer Science - Foundations of genetic algorithms
Algorithmic analysis of a basic evolutionary algorithm for continuous optimization
Theoretical Computer Science
Analysis of a simple evolutionary algorithm for minimization in euclidean spaces
ICALP'03 Proceedings of the 30th international conference on Automata, languages and programming
On the analysis of self-adaptive evolution strategies on elliptic model: first results
Proceedings of the 12th annual conference on Genetic and evolutionary computation
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Evolution Strategies (ES) for black-box optimization of a function f:Rn-R are investigated. Namely, we consider the cumulative step-size adaptation (CSA) for the variance of multivariate zero-mean normal distributions, which are commonly used to sample new candidate solutions within Evolution Strategies (ES). Four simplifications of CSA are proposed and investigated empirically and evaluated statistically. The background for these four new CSA-derivatives, however, is NOT performance tuning, but our aim to accomplish a probabilistic/theoretical runtime analysis of an ES using some kind of a CSA in the near future, and a better understanding of this step-size control mechanisms. Therefore, we consider two test problems, namely the Sphere function without and with Gaussian noise.