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<dc:title xml:lang="pl"><![CDATA[The performance profile: A multi-criteria performance evaluation method for test-based problems]]></dc:title>
<dc:creator><![CDATA[Jaśkowski, Wojciech]]></dc:creator>
<dc:creator><![CDATA[Liskowski, Paweł]]></dc:creator>
<dc:creator><![CDATA[Szubert, Marcin]]></dc:creator>
<dc:creator><![CDATA[Krawiec, Krzysztof]]></dc:creator>
<dc:subject xml:lang="pl"><![CDATA[coevolutionary algorithms]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[evolution strategies]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[Othello]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[Reversi]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[games]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[multi-objective analysis]]></dc:subject>
<dc:description xml:lang="pl"><![CDATA[In test-based problems, solutions produced by search algorithms are typically assessed using average outcomes of interactions with multiple tests. This aggregation leads to information loss, which can render different solutions apparently indifferent and hinder comparison of search algorithms.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[In this paper we introduce the "performance profile", a generic, domain-independent, multi-criteria performance evaluation method that mitigates this problem by characterizing the performance of a solution by a "vector" of outcomes of interactions with tests of various difficulty. To demonstrate the usefulness of this gauge, we employ it to analyze the behavior of Othello and Iterated Prisoner`s Dilemma players produced by five (co)evolutionary algorithms as well as players known from previous publications.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[Performance profiles reveal interesting differences between the players, which escape the attention of the scalar performance measure of the expected utility. In particular, they allow us to observe that evolution with random sampling produces players coping well against the mediocre opponents, while the coevolutionary and temporal difference learning strategies play better against the high-grade opponents.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[We postulate that performance profiles improve our understanding of characteristics of search algorithms applied to arbitrary test-based problems, and can prospectively help design better methods for interactive domains.]]></dc:description>
<dc:publisher><![CDATA[Zielona Góra: Uniwersytet Zielonogórski]]></dc:publisher>
<dc:contributor><![CDATA[Korbicz, Józef (1951- ) - red.]]></dc:contributor>
<dc:contributor><![CDATA[Uciński, Dariusz - red.]]></dc:contributor>
<dc:date><![CDATA[2016]]></dc:date>
<dc:type xml:lang="pl"><![CDATA[artykuł]]></dc:type>
<dc:identifier><![CDATA[http://www.zbc.uz.zgora.pl/repozytorium/Content/79125/AMCS_2016_26_1_15.pdf]]></dc:identifier>
<dc:identifier><![CDATA[https://zbc.uz.zgora.pl/repozytorium/dlibra/publication/88952/edition/79125/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:zbc.uz.zgora.pl:79125]]></dc:identifier>
<dc:source xml:lang="pl"><![CDATA[AMCS, volume 26, number 1 (2016)]]></dc:source>
<dc:source xml:lang="pl"><![CDATA[https://www.amcs.uz.zgora.pl/?action=papers&issue=64]]></dc:source>
<dc:language><![CDATA[eng]]></dc:language>
<dc:relation><![CDATA[oai:zbc.uz.zgora.pl:publication:88952]]></dc:relation>
<dc:rights xml:lang="pl"><![CDATA[Biblioteka Uniwersytetu Zielonogórskiego]]></dc:rights>
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