Krzysztof Krawiec


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Problems in which some entities interact with each other are common in computational intelligence. This scenario, typical for co-evolving artificial-life agents, learning strategies for games, and machine learning from examples, can be formalized as test-based problem. In test-based problems, candidate solutions are evaluated on a number of test cases (agents, opponents, examples). It has been recently shown that at least some of such problems posses underlying problem structure, which can be formalized in a notion of coordinate system, which spatially arranges candidate solutions and tests in a multidimensional space. Such a coordinate system can be extracted to reveal underlying objectives of the problem, which can be then further exploited to help coevolutionary algorithm make progress. In this study, we propose a novel coevolutionary archive method, called Coordinate System Archive (COSA) that is based on these concepts. In the experimental part, we compare COSA to two state-of-the-art archive methods, IPCA and LAPCA. Using two different objective performance measures, we find out that COSA is superior to these methods on a class of artificial problems (COMPARE-ON-ONE). JaskowskiK10Bib

@INPROCEEDINGS { DBLP:conf/cec/JaskowskiK10,
    AUTHOR = { Wojciech Jaƛkowski and Krzysztof Krawiec },
    TITLE = { Coordinate System Archive for coevolution },
    EE = { http://dx.doi.org/10.1109/CEC.2010.5586066 },
    BOOKTITLE = { IEEE Congress on Evolutionary Computation (CEC 2010) },
    PUBLISHER = { IEEE Press },
    ADDRESS = { Barcelona, Spain },
    MONTH = { {18-23 } # jul },
    YEAR = { 2010 },
    PAGES = { 1-10 },
    ABSTRACT = { Problems in which some entities interact with each other are common in computational intelligence. This scenario, typical for co-evolving artificial-life agents, learning strategies for games, and machine learning from examples, can be formalized as test-based problem. In test-based problems, candidate solutions are evaluated on a number of test cases (agents, opponents, examples). It has been recently shown that at least some of such problems posses underlying problem structure, which can be formalized in a notion of coordinate system, which spatially arranges candidate solutions and tests in a multidimensional space. Such a coordinate system can be extracted to reveal underlying objectives of the problem, which can be then further exploited to help coevolutionary algorithm make progress. In this study, we propose a novel coevolutionary archive method, called Coordinate System Archive (COSA) that is based on these concepts. In the experimental part, we compare COSA to two state-of-the-art archive methods, IPCA and LAPCA. Using two different objective performance measures, we find out that COSA is superior to these methods on a class of artificial problems (COMPARE-ON-ONE). },
    KEYWORDS = { artificial life;evolutionary computation;learning (artificial intelligence);IPCA;LAPCA;coevolutionary algorithm;coevolving artificial life agents;computational intelligence;coordinate system archive;machine learning;test based problem;Approximation algorithms;Approximation methods;Context;Games;Machine learning;Machine learning algorithms;Partitioning algorithms },
    DOI = { 10.1109/CEC.2010.5586066 },
}


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