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Introduction to Evolutionary Computation

  • Sebastián Ventura
  • José María Luna
Chapter
  • 1k Downloads

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.

Keywords

Particle Swarm Optimization Genetic Programming Mutation Operator Internal Node Crossover Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 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. 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.zbMATHGoogle Scholar
  3. 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. 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. 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. 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.CrossRefGoogle Scholar
  7. 7.
    H. G. Beyer and H. P. Schwefel. Evolution strategies - a comprehensive introduction. Natural Computing: an international journal, 1(1):3–52, 2002.MathSciNetCrossRefzbMATHGoogle Scholar
  8. 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.MathSciNetGoogle Scholar
  9. 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. 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. 11.
    M. Dorigo and T. Stützle. Ant Colony Optimization. Bradford Company, Scituate, MA, USA, 2004.zbMATHGoogle Scholar
  12. 12.
    M. Dorigo and C. Blum. Ant colony optimization theory: A survey. Theoretical Computer Science, 344(2–3):243–278, 2005.MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    D. Dumitrescu, B. Lazzerini, L. C. Jain, and A. Dumitrescu. Evolutionary Computation. CRC Press, Inc., Boca Raton, FL, USA, 2000.zbMATHGoogle Scholar
  14. 14.
    A. E. Eiben and J. E. Smith. Introduction to Evolutionary Computing. SpringerVerlag, 2003.CrossRefzbMATHGoogle Scholar
  15. 15.
    D. Floreano and C. Mattiussi. Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies. The MIT Press, 2008.Google Scholar
  16. 16.
    L. J. Fogel, A. J. Owens, and M. J. Walsh. Artificial intelligence through simulated evolution. Wiley, Chichester, WS, UK, 1966.zbMATHGoogle Scholar
  17. 17.
    A. A. Freitas. Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer-Verlag Berlin Heidelberg, 2002.CrossRefzbMATHGoogle Scholar
  18. 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.CrossRefGoogle Scholar
  19. 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. 20.
    M. Gendreau and J. Potvin. Handbook of Metaheuristics. Springer Publishing Company, Incorporated, 2nd edition, 2010.CrossRefzbMATHGoogle Scholar
  21. 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. 22.
    D. E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1st edition, 1989.zbMATHGoogle Scholar
  23. 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.CrossRefGoogle Scholar
  24. 24.
    J. H. Holland. Adaptation in Natural and Artificial Systems. The University of Michigan Press, 1975.Google Scholar
  25. 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. 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. 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. 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. 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. 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.CrossRefGoogle Scholar
  31. 31.
    Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, London, UK, UK, 1996.CrossRefzbMATHGoogle Scholar
  32. 32.
    B. L. Miller and D. E. Goldberg. Genetic algorithms, selection schemes, and the varying effects of noise. Evolutionary Computation, 4:113–131, 1996.CrossRefGoogle Scholar
  33. 33.
    M. Mitchell. An Introduction to Genetic Algorithms. MIT Press, Cambridge, MA, USA, 1998.zbMATHGoogle Scholar
  34. 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. 35.
    R. Poli, W. B. Langdon, and N. F. McPhee. A Field Guide to Genetic Programming. Lulu Enterprises, UK Ltd, 2008.Google Scholar
  36. 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. 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.CrossRefGoogle Scholar
  38. 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.CrossRefGoogle Scholar
  39. 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.CrossRefGoogle Scholar
  40. 40.
    M. L. Wong and K. S. Leung. Data Mining Using Grammar-Based Genetic Programming and Applications. Kluwer Academic Publishers, Norwell, MA, USA, 2000.zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sebastián Ventura
    • 1
  • José María Luna
    • 1
  1. 1.Department of Computer Science and Numerical AnalysisUniversity of CordobaCordobaSpain

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