Towards statistical convergence criteria for mutation-based evolutionary algorithms

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This work presents theoretical results on the development of a statistical convergence criterion for evolutionary algorithms. An analytical formula is derived for the probability of success in isotropic Gaussian mutation operators over spherical functions, and statistical criteria are proposed for evaluating, with predefined confidence levels, the convergence of (1+1) and (1+λ) Evolution Strategies. The results presented are intended as a first approach to the development of statistically based stop criteria for evolutionary optimizers, and as a contribution for the broader application of statistical modeling to the development and study of population-based algorithms.
Original languageEnglish
Title of host publicationProceedings of the 2015 Latin America Congress on Computational Intelligence (LA-CCI)
PublisherIEEE
ISBN (Electronic)978-1-4673-8417-9
ISBN (Print)9781467384186
DOIs
Publication statusPublished - 21 Mar 2016
Event2015 Latin America Congress on Computational Intelligence (LA-CCI) - Curitiba, Brazil
Duration: 13 Oct 201516 Oct 2015

Conference

Conference2015 Latin America Congress on Computational Intelligence (LA-CCI)
CountryBrazil
CityCuritiba
Period13/10/1516/10/15

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  • Prizes

    Best Paper Award

    Campelo, Felipe (Recipient), 2015

    Prize: Prize (including medals and awards)

  • Activities

    • 1 Participation in conference

    2015 Latin America Congress on Computational Intelligence (LA-CCI)

    Felipe Campelo Franca Pinto (Participant)

    2015

    Activity: Participating in or organising an event typesParticipation in conference

    Cite this

    Campelo, F. (2016). Towards statistical convergence criteria for mutation-based evolutionary algorithms. In Proceedings of the 2015 Latin America Congress on Computational Intelligence (LA-CCI) IEEE. https://doi.org/10.1109/la-cci.2015.7435944