Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Toward a theory of evolution strategies: The (μ, λ)-theory
Evolutionary Computation
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
On the benefits of populations for noisy optimization
Evolutionary Computation
Latent variable crossover for k-tablet structures and its application to lens design problems
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Analysis of the (μ/μ, λ) - ES on the Parabolic Ridge
Evolutionary Computation
Hierarchically organised evolution strategies on the parabolic ridge
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Step length adaptation on ridge functions
Evolutionary Computation
Evolution strategies with cumulative step length adaptation on the noisy parabolic ridge
Natural Computing: an international journal
Adaptation of expansion rate for real-coded crossovers
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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
Mixed integer evolution strategies for parameter optimization
Evolutionary Computation
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The progress rate of the (1, λ) - ES (Evolution Strategy) is analyzed on the parabolic ridge test function. A different progress behavior is observed for the (1, λ) - ES than for the sphere model test function. The characteristics of the progress rate picture for the plus strategy differs little from the one obtained for the sphere model, but this strategy has drastically worse progress rate values than those obtained for the comma strategy. The dynamics of the distance to the progress axis is also investigated. A theoretical formula is derived to estimate the change in this distance over generations. This formula is used to derive the expected value of the problem-specific distance to the ridge axis. The correctness of the formulae is supported by simulation results.