Memetic algorithms with local search chains in R: The Rmalschains package

Christoph Bergmeir, Daniel Molina, José M. Benítez

    Research output: Contribution to journalArticleResearchpeer-review

    Abstract

    Global optimization is an important field of research both in mathematics and computer sciences. It has applications in nearly all fields of modern science and engineering. Memetic algorithms are powerful problem solvers in the domain of continuous optimization, as they offer a trade-off between exploration of the search space using an evolutionary algorithm scheme, and focused exploitation of promising regions with a local search algorithm. In particular, we describe the memetic algorithms with local search chains (MA-LS-Chains) paradigm, and the R package Rmalschains, which implements them. MA-LS-Chains has proven to be effective compared to other algorithms, especially in high-dimensional problem solving. In an experimental study, we demonstrate the advantages of using Rmalschains for high-dimension optimization problems in comparison to other optimization methods already available in R.

    Original languageEnglish
    Pages (from-to)1-33
    Number of pages33
    JournalJournal of Statistical Software
    Volume75
    Issue number4
    DOIs
    Publication statusPublished - 1 Dec 2016

    Keywords

    • Continuous optimization
    • MA-LS-Chains
    • Memetic algorithms
    • R
    • Rmalschains

    Cite this

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    Memetic algorithms with local search chains in R : The Rmalschains package. / Bergmeir, Christoph; Molina, Daniel; Benítez, José M.

    In: Journal of Statistical Software, Vol. 75, No. 4, 01.12.2016, p. 1-33.

    Research output: Contribution to journalArticleResearchpeer-review

    TY - JOUR

    T1 - Memetic algorithms with local search chains in R

    T2 - The Rmalschains package

    AU - Bergmeir, Christoph

    AU - Molina, Daniel

    AU - Benítez, José M.

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    KW - Memetic algorithms

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