Online evolution for multi-action adversarial games

Niels Justesen, Tobias Mahlmann, Julian Togelius

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


    We present Online Evolution, a novel method for playing turn-based multi-action adversarial games. Such games, which include most strategy games, have extremely high branching factors due to each turn having multiple actions. In Online Evolution, an evolutionary algorithm is used to evolve the combination of atomic actions that make up a single move, with a state evaluation function used for fitness. We implement Online Evolution for the turn-based multi-action game Hero Academy and compare it with a standard Monte Carlo Tree Search implementation as well as two types of greedy algorithms. Online Evolution is shown to outperform these methods by a large margin. This shows that evolutionary planning on the level of a single move can be very effective for this sort of problems.

    Original languageEnglish (US)
    Title of host publicationApplications of Evolutionary Computation - 19th European Conference, EvoApplications 2016, Proceedings
    EditorsPaolo Burelli, Giovanni Squillero
    PublisherSpringer Verlag
    Number of pages14
    ISBN (Print)9783319312033
    StatePublished - 2016
    Event19th European Conference on Applications of Evolutionary Computation, EvoApplications 2016 - Porto, Portugal
    Duration: Mar 30 2016Apr 1 2016

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Other19th European Conference on Applications of Evolutionary Computation, EvoApplications 2016

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science


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