Soft Computing for Knowledge Discovery and Data Mining

pp 79-111

A Review of evolutionary Algorithms for Data Mining

  • Alex A. FreitasAffiliated withComputing Laboratory, University of Kent

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.

Key words: genetic algorithm, genetic programming, classification, clustering, attribute selection, attribute construction, multi-objective optimization