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Evolutionary Algorithms for Chaos Researchers

  • Ivan Zelinka
  • Hendrik Richter
Part of the Studies in Computational Intelligence book series (SCI, volume 267)

Abstract

Evolutionary algorithms are search methods that can be used for solving optimization problems. They mimic working principles from natural evolution by employing a population—based approach, labeling each individual of the population with a fitness and including elements of random, albeit the random is directed through a selection process. In this chapter, we review the basic principles of evolutionary algorithms and discuss their purpose, structure and behavior. In doing so, it is particularly shown how the fundamental understanding of natural evolution processes has cleared the ground for the origin of evolutionary algorithms. Major implementation variants and their structural as well as functional elements are discussed. We also give a brief overview on usability areas of the algorithm and end with some general remarks of the limits of computing.

Keywords

Genetic Algorithm Evolutionary Algorithm Genetic Program Differential Evolution Travel Salesman Problem 
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.

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References

  1. 1.
    Babu, B.: Evolutionary Computation — At a Glance. NEXUS, Annual Magazine of Engineering Technology Association, BITS, Pilani, 3–7 (2001)Google Scholar
  2. 2.
    Back, T., Fogel, B., Michalewicz, Z.: Handbook of Evolutionary Computation, Institute of Physics, London (1997)CrossRefGoogle Scholar
  3. 3.
    Baluja, S.: Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning. Technical Report CMUCS-94-163, Carnegie Mellon University, USA (1994)Google Scholar
  4. 4.
    Barricelli, N.A.: Esempi Numerici di processi di evoluzione. Methodos, 45–68 (1954)Google Scholar
  5. 5.
    Barricelli, N.A.: Symbiogenetic evolution processes realized by artificial methods. Methodos 9(35–36), 143–182 (1957)Google Scholar
  6. 6.
    Barricelli, N.A.: Numerical testing of evolution theories: Part I: Theoretical introduction and basic tests. Acta Biotheor. 16(1–2), 69–98 (1962)CrossRefGoogle Scholar
  7. 7.
    Box, G.E.P.: Evolutionary Operation: A Method for Increasing Industrial Productivity. Appl. Stat. 6(2), 81–101 (1957)CrossRefGoogle Scholar
  8. 8.
    Bremermann, H.: Optimization through evolution and recombination Self-Organizing Systems. In: Yovits, M., Jacobi, G., Goldstine, G. (eds.), pp. 93–106. Spartan Book, Washington (1962)Google Scholar
  9. 9.
    Bull, L., Kovacs, T.: Foundations of Learning Classifier Systems. Springer, Heidelberg (2005)zbMATHCrossRefGoogle Scholar
  10. 10.
    Carlson, E.: Doubts about Mendel’s integrity are exaggerated. In: Mendel’s Legacy, pp. 48–49. Cold Spring Harbor Laboratory Press, Cold Spring Harbor (2004)Google Scholar
  11. 11.
    Caruana, R., Schaffer, J.: Representation and hidden bias: Gray vs. binary coding for genetic algorithms. In: Proc. 5th Int. Conf. on Machine Learning, Los Altos, pp. 153–161. Morgan Kaufmann, San Francisco (1988)Google Scholar
  12. 12.
    Castro, L., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach. Springer, Heidelberg (2002)zbMATHGoogle Scholar
  13. 13.
    Cerny, V.: Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. J. Opt. Theory Appl. 45(1), 41–51 (1985)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Chu, P.: A Genetic Algorithm Approach for Combinatorial Optimisation Problems. Ph.D. Thesis. The Management School Imperial College of Science, Technology and Medicine, London, p. 181 (1997)Google Scholar
  15. 15.
    Clerc, M.: Particle Swarm Optimization. ISTE Publishing Company (2009)Google Scholar
  16. 16.
    Coveney, P., Highfield, R.: Mezi chaosem a radem, Mlada fronta (2003)Google Scholar
  17. 17.
    Darwin, C.: On the origin of species by means of natural selection, or the preservation of favoured races in the struggle for life, 1st edn. John Murray, London (1859)Google Scholar
  18. 18.
    Dasgupta, D.: Artificial Immune Systems and Their Applications. Springer, Berlin (1999)zbMATHGoogle Scholar
  19. 19.
    Davis, L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, Berlin (1996)Google Scholar
  20. 20.
    Dempsey, I., O’Neill, M., Brabazon, A.: Foundations in Grammatical Evolution for Dynamic Environments. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  21. 21.
    Dorigo, M., Stützle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)zbMATHGoogle Scholar
  22. 22.
    Dreo, J., Petrowski, A., Siarry, P., Tailard, E.: Metaheuristic for Hard Optimization: Methods and Case Studies. Springer, Heidelberg (2005)Google Scholar
  23. 23.
    Eiben, A., Smith, J.: Introduction to Evolutionary Computing. Springer, Heidelberg (2007)Google Scholar
  24. 24.
    Feoktistov, V.: Differential Evolution — In Search of Solutions. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  25. 25.
    Fogel, B., Corne, W.: Evolutionary Computation in Bioinformatics. Morgan Kaufmann, San Francisco (2002)Google Scholar
  26. 26.
    Fogel, D.B.: Unearthing a Fossil from the History of Evolutionary Computation. Fundamenta Informaticae 35(1–4), 1–16 (1998)zbMATHMathSciNetGoogle Scholar
  27. 27.
    Fogel, D.B.: Evolutionary computation: the fossil record. IEEE Press, Piscataway (1998)zbMATHGoogle Scholar
  28. 28.
    Fogel, D.B.: Nils Barricelli — Artificial Life, Coevolution, Self-Adaptation. IEEE Comput. Intell. Mag. 1(1), 41–45 (2006)CrossRefGoogle Scholar
  29. 29.
    Fogel, L., Owens, J., Walsh, J.: Artificial Intelligence through Simulated Evolution. John Wiley, Chichester (1966)zbMATHGoogle Scholar
  30. 30.
    Friedberg, R.M.: A learning machine: Part I. IBM Journal Research and Development 2, 2–13 (1958)CrossRefMathSciNetGoogle Scholar
  31. 31.
    Glover, F., Laguna, M.: Tabu Search. Springer, Heidelberg (1997)zbMATHGoogle Scholar
  32. 32.
    Goh, C., Ong, Y., Tan, K.: Multi-Objective Memetic Algorithms. Springer, Heidelberg (2009)zbMATHCrossRefGoogle Scholar
  33. 33.
    Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Publishing Company Inc., Reading (1989)zbMATHGoogle Scholar
  34. 34.
    Haupt, R., Haupt, S.: Practical genetic algorithms, 2nd edn. John Wiley & Sons, USA (2004)zbMATHGoogle Scholar
  35. 35.
    Hart, W., Krasnogor, N., Smith, J.: Recent Advances in Memetic Algorithms. Springer, Heidelberg (2005)zbMATHCrossRefGoogle Scholar
  36. 36.
    Hinterding, R., Gielewski, H., Peachey, T.: The nature of mutation in genetic algorithms. In: Eshelman, L. (ed.) Proc. 6th Int. Conf. on Genetic Algorithms, Los Altos, pp. 70–79. Morgan Kaufmann, San Francisco (1989)Google Scholar
  37. 37.
    Holland, J.: Adaptation in natural and artificial systems. Univ. of Michigan Press, Ann Arbor (1975)Google Scholar
  38. 38.
    Holland, J.: Genetic Algorithms. Sci. Am., 44–50 (1992)Google Scholar
  39. 39.
    Ilachinski, A.: Cellular Automata: A Discrete Universe. World Scientific Publishing Company, Singapore (2001)zbMATHGoogle Scholar
  40. 40.
    Jones, T.: Evolutionary Algorithms, Fitness Landscapes and Search, Ph.D. Thesis, University of New Mexico, Alburquerque (1995)Google Scholar
  41. 41.
    Kirkpatrick, S., Gelatt Jr., C., Vecchi, M.: Optimization by Simulated Annealing. Science 220(4598), 671–680 (1983)CrossRefMathSciNetGoogle Scholar
  42. 42.
    Koza, J.: Genetic Programming. MIT Press, Cambridge (1998)Google Scholar
  43. 43.
    Koza, J.: Genetic Programming: A paradigm for genetically breeding populations of computer programs to solve problems. Stanford University, Computer Science Department, Technical Report STAN-CS-90-1314 (1990)Google Scholar
  44. 44.
    Koza, J., Keane, M., Streeter, M.: Evolving inventions, pp. 40–47. Scientific American (2003)Google Scholar
  45. 45.
    Laguna, M., Martí, R.: Scatter Search — Methodology and Implementations in C. Springer, Heidelberg (2003)Google Scholar
  46. 46.
    Lampinen, J., Zelinka, I.: Mechanical Engineering Design Optimization by Differential Evolution. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 127–146. McGraw-Hill, London (1999)Google Scholar
  47. 47.
    Lampinen, J., Zelinka, I.: Mechanical Engineering Design Optimization by Differential Evolution. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization. McGraw-Hill, London (1999)Google Scholar
  48. 48.
    Langdon, W.: Genetic Programming and Data Structures. Springer, Heidelberg (1998)zbMATHGoogle Scholar
  49. 49.
    Larrañaga, P., Lozano, J.A.: Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Dordrecht (2002)zbMATHGoogle Scholar
  50. 50.
    Lloyd, S., Giovannetti, V., Maccone, L.: Physical limits to communication. Phys. Rev. Lett. 93, 100501 (2004)CrossRefGoogle Scholar
  51. 51.
    Marik, V., Stepankova, O., Lazansky, J.: Artificial Intelligence III. Czech (ed.) Artificial Intelligence III. Academia, Praha (2001)Google Scholar
  52. 52.
    Mendel, J.: Versuche über Plflanzenhybriden Verhandlungen des naturforschenden Vereines in Brünn, Bd. IV für das Jahr. Abhandlungen, 3–47 (1865); For the English translation, see: Druery, C.T., Bateson, W.: Experiments in plant hybridization. Journal of the Royal Horticultural Society 26, 1–32 (1901), http://www.esp.org/ foundations/genetics/classical/gm-65.pdfGoogle Scholar
  53. 53.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin (1996)zbMATHGoogle Scholar
  54. 54.
    Michalewicz, Z., Fogel, D.: How to Solve It: Modern Heuristics. Springer, Berlin (2000)zbMATHGoogle Scholar
  55. 55.
    O’Neill, M., Ryan, C.: Grammatical Evolution — Evolutionary Automatic Programming in an Arbitrary Language. Springer, Heidelberg (2003)Google Scholar
  56. 56.
    Onwubolu, G., Babu, B.: New Optimization Techniques in Engineering. Springer, New York (2004)zbMATHGoogle Scholar
  57. 57.
    Price, K.: An introduction to differential evolution. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimisation, pp. 79–108. McGraw Hill, International, UK (1999)Google Scholar
  58. 58.
    Price, K., Storn, R., et al.: Differential Evolution — A Practical Approach to Global Optimization. Springer, Heidelberg (2005)zbMATHGoogle Scholar
  59. 59.
    Read, R.C.: Coding of Unlabeled Trees. In: Read, R. (ed.) Graph Theory and Computing. Academic Press, London (1972)Google Scholar
  60. 60.
    Rechenberg, I.: (1971) Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution (PhD thesis), Printed in Fromman-Holzboog (1973)Google Scholar
  61. 61.
    Reeves, C.: Modern Heuristic Techniques for Combinatorial Problems. Blackwell Scientific Publications, Oxford (1993)zbMATHGoogle Scholar
  62. 62.
    Rego, C., Alidaee, B.: Metaheuristic Optimization via Memory and Evolution: Tabu Search and Scatter Search. Springer, Heidelberg (2005)zbMATHGoogle Scholar
  63. 63.
    Russell, Norvig, S.J., Peter: Artificial Intelligence: A Modern Approach, 2nd edn., pp. 111–114. Prentice Hall, Upper Saddle River (2003)Google Scholar
  64. 64.
    Schwefel, H.: Numerische Optimierung von Computer-Modellen, PhD thesis (1974); Reprinted by Birkhäuser (1977)Google Scholar
  65. 65.
    Schönberger, J.: Operational Freight Carrier Planning, Basic Concepts. In: Optimization Models and Advanced Memetic Algorithms. Springer, Heidelberg (2005)Google Scholar
  66. 66.
    Telfar, G.: Acceleration Techniques for Simulated Annealing. MSc Thesis. Victoria University of Wellington, New Zealand (1996)Google Scholar
  67. 67.
    Turing, A.: Intelligent machinery, unpublished report for National Physical Laboratory. In: Michie, D. (ed.) Machine Intelligence, vol. 7 (1969); Turing, A.M. (ed.): The Collected Works, vol. 3, Ince D. North-Holland, Amsterdam (1992)Google Scholar
  68. 68.
    Vesterstrom, J., Riget, J.: Particle Swarms (May 2002), Dostupny z www.evalife.dk/publications/JSV_JR_thesis_2002.pdf (cit.10.2.2007)Google Scholar
  69. 69.
    Von Neumann, J.: The computer and the brain. Yale University Press, New Haven (1958)zbMATHGoogle Scholar
  70. 70.
    Wolpert, D., Macready, W.: No Free Lunch Theorems for Search, Technical Report SFITR-95-02-010, Santa Fe Institute (1995)Google Scholar
  71. 71.
    Li, X.: Particle Swarm Optimization — An introduction and its recent developments (2006), www.nical.ustc.edu.cn/seal06/doc/tutorial_pso.pdf (4.10.2006) (cit. 20. 2. 2007)Google Scholar
  72. 72.
    Zelinka, I.: Artificial Intelligence in problems of global optimization. Czech (ed.) BEN, Praha (2002) ISBN 80-7300-069-5Google Scholar
  73. 73.
    Zelinka, I.: SOMA — Self Organizing Migrating Algorithm. In: Onwubolu, Babu, B. (eds.) New Optimization Techniques in Engineering. Springer, New York (2004)Google Scholar
  74. 74.
    Zvelebil, M., Jeremy, B.: Understanding Bioinformatics. Garland Science (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Ivan Zelinka
    • 1
    • 2
  • Hendrik Richter
    • 3
  1. 1.Faculty of Applied InformaticsTomas Bata University in ZlinZlinCzech Republic
  2. 2.Faculty of Electrical Engineering and Computer ScienceVSB-TUOOstrava-PorubaCzech Republic
  3. 3.Fakultät Elektrotechnik und InformationstechnikHTWK LeipzigLeipzigGermany

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