The number of offspring as strategy parameter in hierarchically organized evolution strategies

Herdy, Michael

doi:10.1145/163428.163431

Cited by 30 publications

(44 citation statements)

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“…The strategy considered in this paper is an instance of the general [μ /ρ + , λ (μ/ρ + , λ) γ ]-ES described in [7,15]. This section first describes the lower and then the upper level strategies.…”

Section: Hierarchically Organised Evo-lution Strategiesmentioning

confidence: 99%

“…To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. adaptation [1,9], and the use of hierarchically organised strategies [7,14,15].…”

Section: Introductionmentioning

confidence: 99%

“…Also notice that adaptation by means of hierarchically organised strategies is not limited to step lengths but can be applied to other strategy parameters as well. Herdy [8] considers the problem of adapting the optimal number of offspring generated per time step and demonstrates empirically that near optimal values on the hyperplane and sphere models can be obtained.…”

Section: Introductionmentioning

confidence: 99%

“…A notable exception is a paper by Herdy [7] in which the performance of strategies using a hierarchical organisation for adapting step lengths is compared empirically with that of strategies using mutative self-adaptation. Several objective functions are considered, including the sphere model as well as the sharp and parabolic ridges.…”

Section: Introductionmentioning

confidence: 99%

“…Realising that isolation periods of different lengths are optimal in different environments, Herdy [7] proposes adding yet another level to the hierarchy of evolutionary strategies, with the goal of optimising the length of the isolation periods. He shows empirically that in the long term (i.e., after many time steps), the strategy that adapts the length of its isolation periods performs well on the sphere as well as on the ridges.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchically organised evolution strategies on the parabolic ridge

Arnold

MacLeod

2006

Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation

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Organising evolution strategies hierarchically has been proposed as a means for adapting strategy parameters such as step lengths. Experimental research has shown that on ridge functions, hierarchically organised strategies can significantly outperform strategies that rely on mutative selfadaptation. This paper presents a first theoretical analysis of the behaviour of a hierarchically organised evolution strategy. Quantitative results are derived for the parabolic ridge that describe the dependence on the length of the isolation periods of the mutation strength and the progress rate. The issue of choosing an appropriate length of the isolation periods is discussed and comparisons with recent results for cumulative step length adaptation are drawn.

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Section: Hierarchically Organised Evo-lution Strategiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hierarchically organised evolution strategies on the parabolic ridge

Arnold

MacLeod

2006

Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation

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Noisy Optimization With Evolution Strategies

Arnold

2002

Genetic Algorithms and Evolutionary Computation

123

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Evolution strategies are general, nature-inspired heuristics for search and optimization. Supported both by empirical evidence and by recent theoretical findings, there is a common belief that evolution strategies are robust and reliable, and frequently they are the method of choice if neither derivatives of the objective function are at hand nor differentiability and numerical accuracy can be assumed. However, despite their widespread use, there is little exchange between members of the "classical" optimization community and people working in the field of evolutionary computation. It is our belief that both sides would benefit from such an exchange. In this paper, we present a brief outline of evolution strategies and discuss some of their properties in the presence of noise. We then empirically demonstrate that for a simple but nonetheless nontrivial noisy objective function, an evolution strategy outperforms other optimization algorithms designed to be able to cope with noise. The environment in which the algorithms are tested is deliberately chosen to afford a transparency of the results that reveals the strengths and shortcomings of the strategies, making it possible to draw conclusions with regard to the design of better optimization algorithms for noisy environments.

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Evolutionary Algorithm Parameters and Methods to Tune Them

Eiben

Smit²

2011

Autonomous Search

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I n this chapter we discuss the notion of Evolutionary Algorithm (EA) parameters and propose a distinction between EAs and EA instances, based on the type of parameters used to specify their details. Furthermore, we consider the most important aspects of the parameter tuning problem and give an overview of existing parameter tuning methods. Finally, we elaborate on the methodological issues involved here and provide recommendations for further development. Background and ObjectivesFinding appropriate parameter values for evolutionary algorithms (EA) is one of the persisting grand challenges of the evolutionary computing (EC) field. In general, EC researchers and practitioners all acknowledge that good parameter values are essential for good EA performance. However, very little effort is spent on studying the effect of EA parameters on EA performance and on tuning them. In practice, parameter values are mostly selected by conventions (mutation rate should be low), ad hoc choices (why not use uniform crossover), and experimental comparisons on a limited scale (testing combinations of three different crossover rates and three different mutation rates). Hence, there is a striking gap between the widely acknowledged importance of good parameter values and the widely exhibited ignorance concerning principled approaches to tune EA parameters.To this end, it is important to recall that the problem of setting EA parameters is commonly divided into two cases, parameter tuning and parameter control [14].

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The number of offspring as strategy parameter in hierarchically organized evolution strategies

Cited by 30 publications

References 0 publications

Hierarchically organised evolution strategies on the parabolic ridge

Hierarchically organised evolution strategies on the parabolic ridge

Noisy Optimization With Evolution Strategies

Evolutionary Algorithm Parameters and Methods to Tune Them

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