On May 16th and 17th, a group formed by more than twenty researchers got together in Urbana-Champaign (Illlinois) to participate in the gathering on evolutionary learning organized by the National Center for Supercomputer Applications and the Illinois Genetic Algorithms Laboratory (NIGEL 2006). The goals were to discus current state-of-the-art research in learning classifier systems and other genetics-based machine learning, and to identify future research trends and applications where evolutionary learning might provide a competitive advantage. The first day attendees gave presentations about challenges and current research topics (see the materials below). The second day, a series of three topic-oriented brainstorming sessions were conducted covering: (1) future of LCS and other GBML, (2) areas of application, and (3) techniques.
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The list of participants included Loretta Auvil, Jaume Bacardit, Alwyn Barry, Lashon Booker, Ester Bernado, Will Browne, Martin Butz, Jorge Casillas, Helen Dam, Dipankar Dasgupta, Deon Garrett, David Goldberg, Noriko Imafuji, Pier Luca Lanzi, Xavier Llora, Kumara Sastry, Kamran Shafi, Kenneth Turvey, Michael Welge, Ashley Williams, Stewart Wilson, and Paul Winward.
Presentations slides and videos of the presentations
Some pictures of the event can be found here or at the NIGEL web site.
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Xavier Llorà: “Welcome and presentation”[Slides]
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Stewart W. Wilson: “Can We Do Captchas?” [Slides]
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David E. Goldberg: “Searle, Intentionality, and the Future of Classifier Systems” [Slides]
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Dipankar Dasgupta: “Artificial Immune Systems in Anomaly Detection” [Slides]
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Lashon Booker: “A Retrospective Look at Classifier System Research” [Slides]
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Martin Butz: “XCS: Current Capabilities and Future Challenges” [Slides]
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Alwyn Barry: “Towards a Formal Framework for Accuracy-based LCS” [Slides]
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Xavier Llorà: “Linkage Learning for Pittsburgh Learning Classifier Systems: Making Problems Tractable” [Slides]
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Jorge Casillas: “Scalability in GBML, Accuracy-Based Michigan Fuzzy LCS, and New Trends” [Slides]
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Ester Bernadó: “Learning Classifier Systems for Unbalanced Datasets” [Slides]
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Pier-Luca Lanzi: “Computed Prediction: so far, so good. Now what?” [Slides]
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Jaume Bacardit: “Pittsburgh Learning Classifier Systems for Protein Structure Prediction: Scalability and Explanatory Power” [Slides]
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