Data Mining with Evolutionary Algorithms: Research Directions
Papers from the AAAI Workshop
Alex A. Freitas, Chair
There has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms. Hence, it seems that it is the right time for the communities of data mining and evolutionary algorithms to meet and exchange ideas. The general goal of the workshop was be to discuss promising and necessary research directions in data mining with evolutionary algorithms. Topics included evolutionary algorithms (EA) for classification, clustering, dependence modeling, regression, time series and other data mining tasks; discovery of comprehensible, interesting knowledge with EA; scaling up EA for very large databases; parallel and/or distributed EA; comparison between EA and other data mining methods; genetic operators tailored for data mining tasks; incorporating domain knowledge in EA; integrating EA with database systems; data mining with evolutionary, intelligent agents; hybrid (neural-genetic, rule induction-genetic, etc.) EA; uncertainty handling with EA; data pre-processing with EA; post-processing of the discovered knowledge with EA; and mining semistructured or unstructured data (e.g. text mining) with EA.