CMA evolution strategy assisted by kriging model and approximate ranking View Full Text


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Article Info

DATE

2018-11

AUTHORS

Changwu Huang, Bouchaïb Radi, Abdelkhalak El Hami, Hao Bai

ABSTRACT

The covariance matrix adaptation evolution strategy (CMA-ES) is a competitive evolutionary algorithm (EA) for difficult continuous optimization problems. However, expensive function evaluation of many real-world optimization problems poses a serious challenge to the application of CMA-ES (and other EAs) to these problems. To address this challenge, surrogate-assisted EAs has attracted increasing attention and become popular. In this paper, a new surrogate-assisted CMA-ES algorithm in which Kriging model is used to enhance CMA-ES via approximate ranking procedure is proposed. In the proposed algorithm, the approximate ranking procedure which estimates the rank of current population by using Kriging model and the exact fitness function together is adopted. In addition, the confidence interval method of training set selection is introduced for surrogate model construction. An initial sampling is performed before entering the evolution loop. In each iteration (generation), after the population sampling, the approximate ranking procedure is called instead of the original fitness evaluation, then, parameters of the sampling distribution are updated. This iterative search process continues until the target fitness is reached or the computational budget is exhausted. The proposed algorithm and confidence interval method of training set selection are analyzed through experimental study. The results demonstrate that the confidence interval method works well in Kriging-assisted CMA-ES, and that the proposed algorithm significantly reduces the number of function evaluations of CMA-ES and outperforms the Kriging-assisted CMA-ES using pre-selection and generation-based control on the tested problems. More... »

PAGES

4288-4304

References to SciGraph publications

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  • 2010. Investigating the Local-Meta-Model CMA-ES for Large Population Sizes in APPLICATIONS OF EVOLUTIONARY COMPUTATION
  • 2005. Model Assisted Evolution Strategies in KNOWLEDGE INCORPORATION IN EVOLUTIONARY COMPUTATION
  • 1998-12. Efficient Global Optimization of Expensive Black-Box Functions in JOURNAL OF GLOBAL OPTIMIZATION
  • 2002-03. Evolution strategies – A comprehensive introduction in NATURAL COMPUTING
  • 2005-01. A comprehensive survey of fitness approximation in evolutionary computation in SOFT COMPUTING
  • 2004. Constrained Evolutionary Optimization by Approximate Ranking and Surrogate Models in PARALLEL PROBLEM SOLVING FROM NATURE - PPSN VIII
  • 2016-10. Uncertainty analysis of deep drawing using surrogate model based probabilistic method in THE INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
  • 2016. Doubly Trained Evolution Control for the Surrogate CMA-ES in PARALLEL PROBLEM SOLVING FROM NATURE – PPSN XIV
  • 2006. Local Meta-models for Optimization Using Evolution Strategies in PARALLEL PROBLEM SOLVING FROM NATURE - PPSN IX
  • 2002-10-04. Metamodel—Assisted Evolution Strategies in PARALLEL PROBLEM SOLVING FROM NATURE — PPSN VII
  • 2006. The CMA Evolution Strategy: A Comparing Review in TOWARDS A NEW EVOLUTIONARY COMPUTATION
  • 2005-01. Faster convergence by means of fitness estimation in SOFT COMPUTING
  • 1998. Accelerating the convergence of evolutionary algorithms by fitness landscape approximation in PARALLEL PROBLEM SOLVING FROM NATURE — PPSN V
  • 2004. Gaussian Processes in Machine Learning in ADVANCED LECTURES ON MACHINE LEARNING
  • 2010. A Survey of Fitness Approximation Methods Applied in Evolutionary Algorithms in COMPUTATIONAL INTELLIGENCE IN EXPENSIVE OPTIMIZATION PROBLEMS
  • 2015-06. A two-layer surrogate-assisted particle swarm optimization algorithm in SOFT COMPUTING
  • 2004. Optimization by Gaussian Processes assisted Evolution Strategies in OPERATIONS RESEARCH PROCEEDINGS 2003
  • 2013. Contemporary Evolution Strategies in NONE
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s10489-018-1193-3

    DOI

    http://dx.doi.org/10.1007/s10489-018-1193-3

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