A Review of evolutionary Algorithms for Data Mining

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InCollection{Soft-Comp-KDDM-2007,

• author = "Alex A. Freitas",
• title = "A Review of evolutionary Algorithms for Data Mining",
• booktitle = "Soft Computing for Knowledge Discovery and Data Mining",
• publisher = "Springer",
• year = "2008",
• editor = "Oded Maimon and Lior Rokach",
• pages = "79--111",
• keywords = "genetic algorithms, genetic programming, genetic algorithm, genetic programming, classification, clustering, attribute selection, attribute construction, multi-objective optimization",
• isbn13 = "978-0-387-69935-6",
• URL = "https://www.cs.kent.ac.uk/people/staff/aaf/pub_papers.dir/Soft-Comp-KDDM-2007.pdf",
• DOI = "doi:10.1007/978-0-387-69935-6_4",
• size = "33 pages",
• abstract = "Evolutionary Algorithms (EAs) are stochastic search algorithms inspired by the process of neo-Darwinian evolution. The motivation for applying EAs to data mining is that they are robust, adaptive search techniques that perform a global search in the solution space. This chapter first presents a brief overview of EAs, focusing mainly on two kinds of EAs, viz. Genetic Algorithms (GAs) and Genetic Programming (GP). Then the chapter reviews the main concepts and principles used by EAs designed for solving several data mining tasks, namely: discovery of classification rules, clustering, attribute selection and attribute construction. Finally, it discusses Multi-Objective EAs, based on the concept of Pareto dominance, and their use in several data mining tasks.",

}

Genetic Programming entries for Alex Alves Freitas

Citations