Automated, Explainable Rule Extraction from MAP-Elites archives

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@InProceedings{Urquhart:2021:evoapplications,

• author = "Neil Urquhart and Emma Hart and Silke Hoehl",
• title = "Automated, Explainable Rule Extraction from {MAP-Elites} archives",
• booktitle = "24th International Conference, EvoApplications 2021",
• year = "2021",
• month = "7-9 " # apr,
• editor = "Pedro Castillo and Juanlu Jimenez-Laredo",
• series = "LNCS",
• volume = "12694",
• publisher = "Springer Verlag",
• address = "virtual event",
• pages = "258--272",
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-030-72698-0",
• DOI = "doi:10.1007/978-3-030-72699-7_17",
• abstract = "Quality-diversity (QD) algorithms that return a large archive of elite solutions to a problem provide insights into how high-performing solutions are distributed throughout a feature-space defined by a user. they are often described as illuminating the feature-space, providing a qualitative illustration of relationships between features and objective quality. However, if there are 1000s of solutions in an archive, extracting a succinct set of rules that capture these relationships in a quantitative manner (i.e. as a set of rules) is challenging. We propose two methods for the automated generation of rules from data contained in an archive; the first uses Genetic Programming and the second, a rule-induction method known as CN2. Rules are generated from large archives of data produced by running MAP-Elites on an urban logistics problem. A quantitative and qualitative evaluation that includes the end-user demonstrate that the rules are capable of fitting the data, but also highlights some mismatches between the model used by the optimiser and that assumed by the user.",
• notes = "http://www.evostar.org/2021/ EvoApplications2021 held in conjunction with EuroGP'2021, EvoCOP2021 and EvoMusArt2021",

}

Genetic Programming entries for Neil Urquhart Emma Hart Silke Hoehl

Citations