Skip to main content
SpringerLink
Log in
Book cover

Pattern Mining with Evolutionary Algorithms pp 45–61Cite as

Introduction to Evolutionary Computation

  • Chapter
  • First Online:

  • 1222 Accesses

Abstract

This chapter presents an overview on evolutionary computation, introducing its basic concepts and serving as a starting point for an inexpert user in this field. Then, the chapter discusses paradigms such as genetic algorithms and genetic programming, which are the most widely used techniques in the mining of patterns of interest. Finally, a brief description about other bio-inspired algorithms is considered, paying special interest in ant colony optimization. The main goal is to help the reader to comprehend some basic principles of evolutionary algorithms and swarm intelligence so next chapters of this book can be understood in an appropriate way.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. M. Affenzeller, S. Winkler, S. Wagner, and A. Beham. Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications. Chapman & Hall/CRC, 1st edition, 2009.

    Google Scholar 

  2. G. Allaire. Numerical analysis and optimization: an introduction to mathematical modelling and numerical simulation. Numerical Mathematics and Scientific Computation. Oxford University Press, New York, NY, 2007.

    MATH  Google Scholar 

  3. T. Bäck, F. Hoffmeister, and H. S. Schwefel. A Survey of Evolution Strategies. In Proceedings of the Fourth International Conference on Genetic Algorithms, pages 2–9, San Francisco, CA, USA, 1991. Morgan Kaufmann.

    Google Scholar 

  4. S. Bansal, D. Gupta, V. K. Panchal, and S. Kumar. Swarm intelligence inspired classifiers in comparison with fuzzy and rough classifiers: a remote sensing approach. In Proceedings of the 2nd International Conference on Contemporary Computing, IC3 2009, pages 284–294, Noida, India, 2009.

    Google Scholar 

  5. W. Banzhaf, F. D. Francone, R. E. Keller, and P. Nordin. Genetic programming: an introduction. On the automatic evolution of computer programs and its applications. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1998.

    Google Scholar 

  6. M. Bessaou and P. Siarry. A genetic algorithm with real-value coding to optimize multimodal continuous functions. Structural and Multidisciplinary Optimization, 23(1):63–74, 2002.

    Article  Google Scholar 

  7. H. G. Beyer and H. P. Schwefel. Evolution strategies - a comprehensive introduction. Natural Computing: an international journal, 1(1):3–52, 2002.

    Article  MathSciNet  MATH  Google Scholar 

  8. A. Cano, J. M. Luna, A. Zafra, and S. Ventura. A classification module for genetic programming algorithms in JCLEC. Journal of Machine Learning Research, 16:491–494, 2015.

    MathSciNet  Google Scholar 

  9. E. F. Crane and N. F. McPhee. The effects of size and depth limits on tree based genetic programming. In T. Yu, R. L. Riolo, and B. Worzel, editors, Genetic Programming Theory and Practice III, volume 9 of Genetic Programming, chapter 15, pages 223–240. Springer, 2005.

    Google Scholar 

  10. R. Crawford-Marks. Size control via size fair genetic operators in the pushGP genetic programming system. In In Proceedings of 2002 the Genetic and Evolutionary Computation Conference, GECCO 2002, pages 65–79, New York, USA, 2002. Morgan Kaufmann Publishers.

    Google Scholar 

  11. M. Dorigo and T. Stützle. Ant Colony Optimization. Bradford Company, Scituate, MA, USA, 2004.

    MATH  Google Scholar 

  12. M. Dorigo and C. Blum. Ant colony optimization theory: A survey. Theoretical Computer Science, 344(2–3):243–278, 2005.

    Article  MathSciNet  MATH  Google Scholar 

  13. D. Dumitrescu, B. Lazzerini, L. C. Jain, and A. Dumitrescu. Evolutionary Computation. CRC Press, Inc., Boca Raton, FL, USA, 2000.

    MATH  Google Scholar 

  14. A. E. Eiben and J. E. Smith. Introduction to Evolutionary Computing. SpringerVerlag, 2003.

    Book  MATH  Google Scholar 

  15. D. Floreano and C. Mattiussi. Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies. The MIT Press, 2008.

    Google Scholar 

  16. L. J. Fogel, A. J. Owens, and M. J. Walsh. Artificial intelligence through simulated evolution. Wiley, Chichester, WS, UK, 1966.

    MATH  Google Scholar 

  17. A. A. Freitas. Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer-Verlag Berlin Heidelberg, 2002.

    Book  MATH  Google Scholar 

  18. A. A. Freitas. A Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery. In A. Ghosh and S. Tsutsui, editors, Advances in Evolutionary Computing, pages 819–845. Springer-Verlag New York, Inc., New York, NY, USA, 2003.

    Chapter  Google Scholar 

  19. M. Fuchs. Crossover versus mutation: an empirical and theoretical case study. In In Proceedings of the third annual conference on Genetic Programming, GP ’98, pages 78–85, 1998.

    Google Scholar 

  20. M. Gendreau and J. Potvin. Handbook of Metaheuristics. Springer Publishing Company, Incorporated, 2nd edition, 2010.

    Book  MATH  Google Scholar 

  21. L. Goel, D. Gupta, V. K. Panchal, and A. Abraham. Taxonomy of nature inspired computational intelligence: a remote sensing perspective. In Proceedings of the 4th World Congress on Nature and Biologically Inspired Computing, NaBIC 2012, pages 200–206, Mexico City, Mexico, 2012.

    Google Scholar 

  22. D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1st edition, 1989.

    MATH  Google Scholar 

  23. P. González-Espejo, S. Ventura, and F. Herrera. A Survey on the Application of Genetic Programming to Classification. IEEE Transactions on Systems, Man and Cybernetics: Part C, 40(2):121–144, 2010.

    Article  Google Scholar 

  24. J. H. Holland. Adaptation in Natural and Artificial Systems. The University of Michigan Press, 1975.

    Google Scholar 

  25. D. Karaboga, B. Akay, and C. Ozturk. Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks. In Proceedings of the 4th International Conference on Modeling Decisions for Artificial Intelligence, MDAI ’07, pages 318–329, Kitakyushu, Japan, 2007.

    Google Scholar 

  26. J. Kennedy and R. C. Eberhart. Particle swarm optimization. In Proceedings of the 1995 IEEE International Conference on Neural Networks, volume 4, pages 1942–1948, 1995.

    Google Scholar 

  27. J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection (Complex Adaptive Systems). A Bradford Book, 1 edition, 1992.

    Google Scholar 

  28. W. B. Langdon, T. Soule, R. Poli, and J. A. Foster. The evolution of size and shape. In L. Spector, W. B. Langdon, U. O’Reilly, and P. J. Angeline, editors, Advances in Genetic Programming III, chapter 8, pages 163–190. MIT Press, Cambridge, MA, USA, June 1999.

    Google Scholar 

  29. S. Luke and L. Spector. A comparison of crossover and mutation in genetic programming. In Proceedings of the Second Annual Conference on Genetic Programming, GP ’97, pages 240–248. Morgan Kaufmann, 1997.

    Google Scholar 

  30. R. McKay, N. Hoai, P. Whigham, Y. Shan, and M. O’Neill. Grammar-based Genetic Programming: a Survey. Genetic Programming and Evolvable Machines, 11:365–396, 2010.

    Article  Google Scholar 

  31. Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, London, UK, UK, 1996.

    Book  MATH  Google Scholar 

  32. B. L. Miller and D. E. Goldberg. Genetic algorithms, selection schemes, and the varying effects of noise. Evolutionary Computation, 4:113–131, 1996.

    Article  Google Scholar 

  33. M. Mitchell. An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA, USA, 1998.

    MATH  Google Scholar 

  34. J. L. Olmo, J. M. Luna, J. R. Romero, and S. Ventura. Mining association rules with single and multi-objective grammar guided ant programming. Integrated Computer-Aided Engineering, 20(3):217–234, 2013.

    Google Scholar 

  35. R. Poli, W. B. Langdon, and N. F. McPhee. A Field Guide to Genetic Programming. Lulu Enterprises, UK Ltd, 2008.

    Google Scholar 

  36. A. Ratle and M. Sebag. Genetic Programming and Domain Knowledge: Beyond the Limitations of Grammar-Guided Machine Discovery. In Proceedings of the 6th International Conference on Parallel Problem Solving from Nature, PPSN VI, pages 211–220, Paris, France, September 2000.

    Google Scholar 

  37. T. Soule and J. A. Foster. Effects of code growth and parsimony pressure on populations in genetic programming. Evolutionary Computation, 6(4):293–309, 1998.

    Article  Google Scholar 

  38. D. Thierens and D. Goldberg. Convergence models of genetic algorithm selection schemes. In Y. Davidor, H. P. Schwefel, and R. Männer, editors, Parallel Problem Solving from Nature - PPSN III, volume 866 of Lecture Notes in Computer Science, pages 119–129. Springer Berlin Heidelberg, 1994.

    Chapter  Google Scholar 

  39. A. Tsakonas, G. Dounias, J. Jantzen, H. Axer, B. Bjerregaard, and D. G. von Keyserlingk. Evolving rule-based systems in two medical domains using genetic programming. Artificial Intelligence in Medicine, 32(3):195–216, 2004.

    Article  Google Scholar 

  40. M. L. Wong and K. S. Leung. Data Mining Using Grammar-Based Genetic Programming and Applications. Kluwer Academic Publishers, Norwell, MA, USA, 2000.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Numerical Analysis, University of Cordoba, Cordoba, Spain

    Sebastián Ventura & José María Luna

Authors
  1. Sebastián Ventura
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. José María Luna
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Ventura, S., Luna, J.M. (2016). Introduction to Evolutionary Computation. In: Pattern Mining with Evolutionary Algorithms. Springer, Cham. https://doi.org/10.1007/978-3-319-33858-3_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-33858-3_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33857-6

  • Online ISBN: 978-3-319-33858-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation