Data Mining and Knowledge Discovery Handbook
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TL;DR
This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently.
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.