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Aiming for a theoretically tractable CSA variant by means of empirical investigations

Authors:

Jens Jägersküpper,

Mike PreussAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 503 - 510

https://doi.org/10.1145/1389095.1389191

Published: 12 July 2008 Publication History

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Abstract

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.

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Cited By

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Huang HSu JZhang YHao Z(2020)An Experimental Method to Estimate Running Time of Evolutionary Algorithms for Continuous OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2019.292154724:2(275-289)Online publication date: Apr-2020
https://doi.org/10.1109/TEVC.2019.2921547
Melkozerov ABeyer HPelikan MBranke J(2010)On the analysis of self-adaptive evolution strategies on elliptic modelProceedings of the 12th annual conference on Genetic and evolutionary computation10.1145/1830483.1830554(369-376)Online publication date: 7-Jul-2010
https://dl.acm.org/doi/10.1145/1830483.1830554

Index Terms

Aiming for a theoretically tractable CSA variant by means of empirical investigations
1. Mathematics of computing
  1. Mathematical analysis
    1. Numerical analysis
  2. Probability and statistics
    1. Probabilistic algorithms
    2. Probabilistic reasoning algorithms
      1. Markov-chain Monte Carlo methods
      2. Sequential Monte Carlo methods
2. Theory of computation
  1. Design and analysis of algorithms

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GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 12 July 2008

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July 12 - 16, 2008

GA, Atlanta, USA

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Huang HSu JZhang YHao Z(2020)An Experimental Method to Estimate Running Time of Evolutionary Algorithms for Continuous OptimizationIEEE Transactions on Evolutionary Computation10.1109/TEVC.2019.292154724:2(275-289)Online publication date: Apr-2020
https://doi.org/10.1109/TEVC.2019.2921547
Melkozerov ABeyer HPelikan MBranke J(2010)On the analysis of self-adaptive evolution strategies on elliptic modelProceedings of the 12th annual conference on Genetic and evolutionary computation10.1145/1830483.1830554(369-376)Online publication date: 7-Jul-2010
https://dl.acm.org/doi/10.1145/1830483.1830554

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Rigorous runtime analysis of a (μ+1)ES for the sphere function

Probabilistic runtime analysis of (1 +, λ),ES using isotropic mutations

Empirical analysis of ideal recombination on random decomposable problems

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