Chapter 6. Genetic Algorithms

  1. Daniel T. Larose Ph.D. Director

Published Online: 30 JAN 2006

DOI: 10.1002/0471756482.ch6

Data Mining Methods and Models

Data Mining Methods and Models

How to Cite

Larose, D. T. (2005) Genetic Algorithms, in Data Mining Methods and Models, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/0471756482.ch6

Author Information

  1. Department of Mathematical Sciences, Central Connecticut State University, USA

Publication History

  1. Published Online: 30 JAN 2006
  2. Published Print: 11 NOV 2005

ISBN Information

Print ISBN: 9780471666561

Online ISBN: 9780471756484



  • selection;
  • crossover;
  • mutation;
  • optimization;
  • global optimum;
  • selection pressure;
  • crowding;
  • fitness;
  • WEKA


Chapter six begins by introducing genetic algorithms by way of analogy with the biological processes at work in the evolution of organisms. The basic framework of a genetic algorithm is provided, including the three basic operators: selection, crossover, and mutation. A simple example of a genetic algorithm at work is examined, with each step explained and demonstrated. Next, modifications and enhancements from the literature are discussed, especially for the selection and crossover operators. Genetic algorithms for real-valued variables are discussed. The use of genetic algorithms as optimizers within a neural network is demonstrated, where the genetic algorithm replaces the using backpropagation algorithm. Finally, an example of the use of WEKA for genetic algorithms is provided.