Data Mining and Knowledge Discovery Handbook, 2 Edition part 40. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 19 A Review of Evolutionary Algorithms for Data Mining Alex A. Freitas University of Kent UK Computing Laboratory Summary. 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 Introduction The paradigm of Evolutionary Algorithms EAs consists of stochastic search algorithms inspired by the process of neo-Darwinian evolution Back et al. 2000 De Jong 2006 Eiben 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 candidate solution an individual represents and how the quality of that solution is evaluated by a fitness function . In essence the motivation for applying EAs to data mining is that EAs are robust adaptive search methods that perform a global search in the space of candidate solutions. In contrast several more conventional data mining methods perform a local greedy search in the space of candidate solutions. As a result of their
