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Multiparent Recombination in Evolutionary Computing

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Advances in Evolutionary Computing

Part of the book series: Natural Computing Series ((NCS))

Abstract

This chapter considers multiparent reproduction, where more than two parents are involved in creating offspring. First we give a survey of multiparent operators that have been introduced over the years in evolutionary computing and we reformulate the traditional mutation-or-crossover debate in the light of such operators. Second, we present some existing results on the usefulness of multiparent operators. We conclude the chapter with a look at future developments and some suggestions for further research.

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References

  1. Ackley, D. H. (1987) An empirical study of bit vector function optimization. In L. Davis, editor, Genetic Algorithms and Simulated Annealing, 170–215 Morgan Kaufmann, San Francisco.

    Google Scholar 

  2. Bäck, T. (1996) Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York.

    MATH  Google Scholar 

  3. Bäck, T., Michalewicz, Z. (1997) Test landscapes. In T. Baeck, D. Fogel, and Z. Michalewicz, editors, Handbook of Evolutionary Computation, pages B2.7:14-B2.7:20. Institute of Physics Publishing, Bristol, and Oxford University Press, New York.

    Chapter  Google Scholar 

  4. Belew, R. K., Booker, L. B. (eds.) (1991) Proceedings of the 4th International Conference on Genetic Algorithms Morgan Kaufmann, San Francisco.

    Google Scholar 

  5. Bersini, H., Seront, G. (1992) In search of a good evolution-optimization crossover. In R. Maenner and B. Manderick, editors, Proceedings of the 2nd Conference on Parallel Problem Solving from Nature, 479–488 North-Holland, Amsterdam.

    Google Scholar 

  6. Bersini, H., Varela, F.J. (1991) The immune recruitment mechanism: A selective evolutionary strategy. In Belew and Booker [4], 520–526.

    Google Scholar 

  7. Beyer, H. G. (1995) Toward a theory of evolution strategies: On the benefits of the (μ/ μ, λ) theory. Evolutionary Computation, 3(1):81–111.

    Article  MathSciNet  Google Scholar 

  8. Bremermann, H. J., Rogson, M., Salaff, S. (1966) Global properties of evolution processes. In H.H. Pattee, E.A. Edlsack, L. Fein, and A.B. Callahan, editors, Natural Automata and Useful Simulations, 3–41 Spartan Books, Washington, DC.

    Google Scholar 

  9. Davidor, Y., Schwefel, H. P., Männer, R. (eds.) (1994) Proceedings of the 3rd Conference on Parallel Problem Solving from Nature, number 866 in Lecture Notes in Computer Science. Springer, Berlin.

    Google Scholar 

  10. Davis, L. (1989) Adapting operator probabilities in genetic algorithms. In Schaffer [36], 61–69.

    Google Scholar 

  11. Eiben, A. E., Bäck, Th. (1997) An empirical investigation of multi-parent recombination operators in evolution strategies. Evolutionary Computation, 5(3):347–365.

    Article  Google Scholar 

  12. Eiben, A. E., Jelasity, M. (1992) A critical note on experimental research methodology in EC. In 2002 Congress on Evolutionary Computation (CEC’2002), 582–587. IEEE Press, Piscataway, NJ.

    Google Scholar 

  13. Eiben, A.E., Rau é, P.E., Ruttkay, Zs. (1994) Genetic algorithms with multiparent recombination. In Davidor et al. [9], 78–87.

    Google Scholar 

  14. Eiben, A. E., Schippers, C. A. (1996) Multi-parent’s niche: n-ary crossovers on NK-landscapes. In H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel, editors, Proceedings of the 4th Conference on Parallel Problem Solving from Nature, number 1141 in Lecture Notes in Computer Science, pages 319–328. Springer, Berlin.

    Google Scholar 

  15. Eiben, A.E., Sprinkhuizen-Kuyper, I.G., Thijssen, B.A. (1998) Competing crossovers in an adaptive GA framework. In [26], 787–792.

    Google Scholar 

  16. Eiben, A. E., van Kemenade, C.H. M. (1997) Diagonal crossover in genetic algorithms for numerical optimization. Journal of Control and Cybernetics, 26(3):447–465.

    MATH  Google Scholar 

  17. Eiben, A. E., van Kemenade, C. H.M. (1995) Performance of multi-parent crossover operators on numerical function optimization problems. Technical Report TR-95-33, Leiden University; available from http://www.liacs.nl/TechRep/1995/.

  18. Eiben, A.E., van Kemenade, C.H.M., KOK, J.N.(1995) Orgy in the Computer:Multi-parent reproduction in genetic algorithm. In Moraen et al. [31], 934–945.

    Google Scholar 

  19. Eshelman, L.J., Mathias, K.E., Schaffer, J.D. (1997) Crossover operator biases: Exploiting the population distribution. In Th. Baeck, editor, Proceedings of the 7th International Conference on Genetic Algorithms, 354–361. Morgan Kaufmann, San Francisco.

    Google Scholar 

  20. Eshelman, L.J., Schaffer, J. D. (1993) Crossover’s niche. In Forrest [23], 9–14.

    Google Scholar 

  21. Fogel, D.B., Atmar, J. W. (1990) Comparing genetic operators with Gaussian mutations in simulated evolutionary processes using linear systems. Biological Cybernetics, 63:111–114.

    Article  Google Scholar 

  22. Fogel, D.B., Atmar, J.W. (1990) Fogel, D. B., Stayton, L. C. (1994) On the effectiveness of crossover in simulated evolutionary optimization. Biosystems, 32:171–182.

    Article  Google Scholar 

  23. Forrest, S. (ed.) (1993) Proceedings of the 5th International Conference on Genetic Algorithms. Morgan Kaufmann, San Francisco.

    Google Scholar 

  24. Furuya, H., Haftka, R. T. (1993) Genetic algorithms for placing actuators on space structures. In Forrest [23], 536–542.

    Google Scholar 

  25. Hordijk, W., Manderick, B. (1995) The usefulness of recombination. In Moraen et al. [31], 908–919.

    Google Scholar 

  26. IEEE Proceedings of the 1995 IEEE Conference on Evolutionary Computation. IEEE Press, Piscataway, NJ.

    Google Scholar 

  27. IEEE Proceedings of the 1996 IEEE Conference on Evolutionary Computation. IEEE Press, Piscataway, NJ.

    Google Scholar 

  28. Kauffman, S.A. (1993) Origins of Order: Self-Organization and Selection in Evolution. Oxford University Press, New York.

    Google Scholar 

  29. Kaufman, H. (1967) An experimental investigation of process identification by competitive evolution. IEEE Transactions on Systems Science and Cybernetics, SSC-3(1), 11–16.

    Article  Google Scholar 

  30. Mitchell, M., Forrest, S., Holland, J. H. (1994) The royal road for genetic algorithms: Fitness landscapes and GA performance. In F.J. Varela and P. Bourgine, editors, Toward a Practice of Autonomous Systems: Proceedings of the 1st European Conference on Artificial Life, pages 245–254. The MIT Press, Cambridge, MA.

    Google Scholar 

  31. Mor án, F., Moreno, A., Merelo, J. J., Chacón, P. (eds) (1995) Advances in Artificial Life. Third International Conference on Artificial Life, volume 929 of Lecture Notes in Artificial Intelligence. Springer, Berlin.

    Google Scholar 

  32. Mühlenbein, H. (1989) Parallel genetic algorithms, population genetics and combinatorial optimization. In Schaffer [36], 416–421.

    Google Scholar 

  33. Müh lenbein, H., Voigt, H. M. (1996) Gene pool recombination in genetic algorithms. In I.H. Osman and J.P. Kelly, editors, Meta-Heuristics: Theory and Applications, 53–62 Boston, London, Dordrecht, Kluwer Academic Publishers.

    Google Scholar 

  34. Pál, K. F. (1994) Selection schemes with spatial isolation for genetic optimization. In Davidor et al. [9], 170–179.

    Google Scholar 

  35. Renders, J. M., Bersini, H. (1994) Hybridizing genetic algorithms with hill-climbing methods for global optimization: Two possible ways. In Proceedings of the First IEEE Conference on Evolutionary Computation, pages 312–317. IEEE Press, Piscataway NJ, 1994.

    Google Scholar 

  36. Schaffer, J. D. (ed) (1989) Proceedings of the 3rd International Conference on Genetic Algorithms. Morgan Kaufmann, San Francisco.

    Google Scholar 

  37. Schaffer, J. D., Eshelman, L.J. (1991) On crossover as an evolutionary viable strategy. In Belew and Booker [4], 61–68.

    Google Scholar 

  38. Schlierkamp-Voosen, D., Mühlenbein, H. (1996) Adaptation of population sizes by competing subpopulations. In [27], 330–335.

    Google Scholar 

  39. Schwefel, H. P. (1995) Evolution and Optimum Seeking. Wiley, New York.

    Google Scholar 

  40. Schwefel, H.P., Rudolph, G. (1995) Contemporary evolution strategies. In Moraenet al. [31], 893–907.

    Google Scholar 

  41. Smith, J., Fogarty, T. C. (1996) Recombination strategy adaptation via evolution of gene linkage. In [27], 826–831.

    Google Scholar 

  42. Spears, W. M. (1993) Crossover or mutation? In L.D. Whitley, editor, Foundations of Genetic Algorithms — 2, pages 221–238. Morgan Kaufmann, San Francisco.

    Google Scholar 

  43. Spears, W. M. (1995) Adapting crossover in evolutionary algorithms. In J.R. McDonnell, R.G. Reynolds, and D.B. Fogel, editors, Proceedings of the 4th Annual Conference on Evolutionary Programming, pages 367–384. MIT Press, Cambridge, MP. 44. Tsutsui, S. (1998) Multi-parent recombination in genetic algorithms with search space boundary extension by mirroring. In A.E. Eiben, Th. Baeck, M. Schoenauer, and H.-P. Schwefel, editors, Proceedings of the 5th Conference on Parallel Problem Solving from Nature, number 1498 in Lecture Notes in Computer Science, 428–437. Springer, Berlin.

    Google Scholar 

  44. Tsutsui, S., Ghosh, A. (1998) A study on the effect of multi-parent recombination in real coded genetic algorithms. In Proceedings of the 1998 IEEE Conference on Evolutionary Computation, 828–833. IEEE Press, Piscataway, NJ.

    Google Scholar 

  45. Tsutsui, S., Yamamura, M., Higuchi, T. (1999) Multi-parent recombination with simplex crossover in real coded genetic algorithms. In W. Banzhaf, J. Daida, A.E. Eiben, H.H. Garzon, V. Honavar, M. Jakiela, and R.E. Smith, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-1999), 657–664. Morgan Kaufmann, San Francisco.

    Google Scholar 

  46. van Kemenade, C. H. M. Kok, J. N., Eiben, A. E. (1995) Raising GA performance by simultaneous tuning of selective pressure and recombination disruptiveness. In [26], 346–351.

    Google Scholar 

  47. Voigt, H. M., Ühlenbein, H. M. (1995) Gene pool recombination and utilization of covariances for the Breeder Genetic Algorithm. In [26), 172–177.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Free University Amsterdam, The Netherlands

    A. E. Eiben

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  1. A. E. Eiben
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Editors and Affiliations

  1. Machine Intelligence Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata, 700 035, India

    Ashish Ghosh

  2. Department of Management Information, Hannan University, 5-4-33 Amamhigigashi, Matsubara, Osaka, 580-8502, Japan

    Shigeyoshi Tsutsui

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© 2003 Springer-Verlag Berlin Heidelberg

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Eiben, A.E. (2003). Multiparent Recombination in Evolutionary Computing. In: Ghosh, A., Tsutsui, S. (eds) Advances in Evolutionary Computing. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18965-4_6

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  • DOI: https://doi.org/10.1007/978-3-642-18965-4_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-62386-8

  • Online ISBN: 978-3-642-18965-4

  • eBook Packages: Springer Book Archive

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