Co-evolutionary Genetic Programming for Dataset Similarity Induction

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@InProceedings{Smid:2015:CEC,

• author = "Jakub Smid and Martin Pilat and Klara Peskova and Roman Neruda",
• title = "Co-evolutionary Genetic Programming for Dataset Similarity Induction",
• booktitle = "Proceedings of 2015 IEEE Congress on Evolutionary Computation (CEC 2015)",
• year = "2015",
• editor = "Yadahiko Murata",
• pages = "1160--1166",
• address = "Sendai, Japan",
• month = "25-28 " # may,
• publisher = "IEEE Press",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-4799-7491-7",
• DOI = "doi:10.1109/CEC.2015.7257020",
• abstract = "Metalearning deals with an important problem in machine-learning, namely selecting the right techniques to model the data at hand. In most of the meta learning approaches, a notion of similarity between datasets is needed. Our approach derives the similarity measure by combining arbitrary attribute similarity functions ordered by the optimal attribute assignment. In this paper, we propose a genetic programming based approach to the evolution of an attribute similarity inducing function. The function is composed of two parts - one describes the similarity of categorical attributes, the other describes the similarity of numerical attributes. Co-evolution is used to put these two parts together to form the similarity function. We use a repairing approach to guarantee some of the metric features for this function, and also discuss which of these features are important in metalearning.",
• notes = "1315 hrs 15474 CEC2015",

}

Genetic Programming entries for Jakub Smid Martin Pilat Klara Peskova Roman Neruda

Citations