Evolution evolves with autoconstruction

Lee Spector, Nicholas Freitag McPhee, Thomas Helmuth, Maggie M. Casale, Julian Oks

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

    6 Scopus citations

    Abstract

    In autoconstructive evolutionary algorithms, individuals implement not only candidate solutions to specified computational problems, but also their own methods for variation of offspring. This makes it possible for the variation methods to themselves evolve, which could, in principle, produce a system with an enhanced capacity for adaptation and superior problem solving power. Prior work on autoconsruction has explored a range of system designs and their evolutionary dynamics, but it has not solved hard problems. Here we describe a new approach that can indeed solve at least some hard problems. We present the key components of this approach, including the use of linear genomes for hierarchically structured programs, a diversity-maintaining parent selection algorithm, and the enforcement of diversification constraints on offspring. We describe a software synthesis benchmark problem that our new approach can solve, and we present visualizations of data from single successful runs of autoconstructive vs. non-autoconstructive systems on this problem. While anecdotal, the data suggests that variation methods, and therefore significant aspects of the evolutionary process, evolve over the course of the autoconstructive runs.

    Original languageEnglish (US)
    Title of host publicationGECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference
    EditorsTobias Friedrich
    PublisherAssociation for Computing Machinery, Inc
    Pages1349-1356
    Number of pages8
    ISBN (Electronic)9781450343237
    DOIs
    StatePublished - Jul 20 2016
    Event2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion - Denver, United States
    Duration: Jul 20 2016Jul 24 2016

    Publication series

    NameGECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference

    Other

    Other2016 Genetic and Evolutionary Computation Conference, GECCO 2016 Companion
    CountryUnited States
    CityDenver
    Period7/20/167/24/16

    Bibliographical note

    Funding Information:
    This material is based upon work supported by the National Science Foundation under Grants No. 1129139 and 1331283.

    Publisher Copyright:
    © 2016 ACM.

    Copyright:
    Copyright 2017 Elsevier B.V., All rights reserved.

    Keywords

    • Autoconstructive evolution
    • Genetic programming
    • Push

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