@inproceedings{ Jaskowski10coordinate,
  author    = {Wojciech Jaśkowski and Krzysztof Krawiec},
  title     = {Coordinate System Archive for Coevolution}, 
  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).},
  booktitle   = {Evolutionary Computation (CEC), 2010 IEEE Congress on},
  organization   = {IEEE},
  pages   = {1-10} ,
  year   = {2010}
}

{"_id":{"_str":"5342697b0e946d920a000fba"},"__v":24,"authorIDs":["545783452abc8e9f3700049c"],"author_short":["Jaśkowski, W.","Krawiec, K."],"bibbaseid":"jakowski-krawiec-coordinatesystemarchiveforcoevolution-2010","bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Wojciech"],"propositions":[],"lastnames":["Jaśkowski"],"suffixes":[]},{"firstnames":["Krzysztof"],"propositions":[],"lastnames":["Krawiec"],"suffixes":[]}],"title":"Coordinate System Archive for Coevolution","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).","booktitle":"Evolutionary Computation (CEC), 2010 IEEE Congress on","organization":"IEEE","pages":"1-10","year":"2010","bibtex":"@inproceedings{ Jaskowski10coordinate,\n author = {Wojciech Jaśkowski and Krzysztof Krawiec},\n title = {Coordinate System Archive for Coevolution}, \n 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).},\n booktitle = {Evolutionary Computation (CEC), 2010 IEEE Congress on},\n organization = {IEEE},\n pages = {1-10} ,\n year = {2010}\n}\n\n\n","author_short":["Jaśkowski, W.","Krawiec, K."],"key":"Jaskowski10coordinate","id":"Jaskowski10coordinate","bibbaseid":"jakowski-krawiec-coordinatesystemarchiveforcoevolution-2010","role":"author","urls":{},"downloads":0},"bibtype":"inproceedings","biburl":"http://data.bibbase.org/keyword/lapca/?format=bibtex","downloads":0,"keywords":[],"search_terms":["coordinate","system","archive","coevolution","jaśkowski","krawiec"],"title":"Coordinate System Archive for Coevolution","year":2010,"dataSources":["zCdYMau57z9HCf3BN"]}