TABLE OF CONTENTS Alex A. Freitas
University of Kent, UK
| Abstract | Evolutionary Algorithms (EAs) are stochastic search algorithms inspired by the process of 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 reviews mainly two kinds of EAs, viz. Genetic Algorithms (GAs) and Genetic Programming (GP), and discusses how EAs can be applied to several Data Mining tasks, namely: discovery of classification rules, clustering, attribute selection and attribute construction. It also discusses the basic idea of Multi-Objective EAs, based on the concept of Pareto dominance, which also has applications in Data Mining. |
| Keywords: | genetic algorithm, genetic programming, classification, clustering, attribute selection, attribute construction, multi-objective optimization |
The paradigm of Evolutionary Algorithms (EAs) consists of stochastic search algorithms inspired by the process of Darwinian evolution (Back and Weigend, 1998; Eiben and Smith, 2003). EAs work with a population of individuals, each of them a candidate solution to a given problem, that "evolve" towards better and better solutions to that problem. It should be noted that this is a very generic search paradigm. EAs can be used to solve many different kinds of problems, by carefully specifying what kind of...
