Random Dynamics Optimum Tracking with Evolution Strategies
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
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
An analysis of mutative σ-self-adaptation on linear fitness functions
Evolutionary Computation
Optimum tracking with evolution strategies
Evolutionary Computation
Evolution strategies with cumulative step length adaptation on the noisy parabolic ridge
Natural Computing: an international journal
On the behaviour of evolution strategies optimising cigar functions
Evolutionary Computation
On the behaviour of the (1,λ)-es for a simple constrained problem
Proceedings of the 11th workshop proceedings 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
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The CSA-ES is an Evolution Strategy with Cumulative Step size Adaptation, where the step size is adapted measuring the length of a so-called cumulative path. The cumulative path is a combination of the previous steps realized by the algorithm, where the importance of each step decreases with time. This article studies the CSA-ES on composites of strictly increasing functions with affine linear functions through the investigation of its underlying Markov chains. Rigorous results on the change and the variation of the step size are derived with and without cumulation. The step-size diverges geometrically fast in most cases. Furthermore, the influence of the cumulation parameter is studied.