Encyclopedia of Operations Research and Management Science

2001 Edition
| Editors: Saul I. Gass, Carl M. Harris

Evolutionary algorithms

  • Zbigniew Michalewicz
  • Marc Schoenauer
Reference work entry
DOI: https://doi.org/10.1007/1-4020-0611-X_308

INTRODUCTION

Evolutionary computation (EC) techniques are stochastic algorithms whose search methods model some natural phenomena: genetic inheritance and Darwinian strife for survival. The idea behind evolutionary algorithms is to do what nature does. Let us take rabbits as an example: at any given time there is a population of rabbits. Some of them are faster and smarter than other rabbits. These faster, smarter rabbits are less likely to be eaten by foxes, and therefore more of them survive to do what rabbits do best: make more rabbits. Of course, some of the slower, dumber rabbits will survive just because they are lucky. This surviving population of rabbits starts breeding. The breeding results in a good mixture of rabbit genetic material: some slow rabbits breed with fast rabbits, some fast with fast, some smart rabbits with dumb rabbits, and so on. And on the top of that, nature throws in a “wild hare” every once in a while by mutating some of the rabbit genetic material. The...

This is a preview of subscription content, log in to check access.

References

  1. [1]
    Angeline, P.J. and Kinnear, K.E., eds. (1996). Advances in Genetic Programming II, MIT Press, Cambridge, Massachusetts.Google Scholar
  2. [2]
    Antonisse, J. (1994). “A new interpretation of schema notation that overturns the binary encoding constraint,” in [43], 86–91. Google Scholar
  3. [3]
    Bäck, T., Rudolph, G., and Schwefel, H.P. (1992). “Evolutionary programming and evolution strategies: Similarities and differences,” in [25], 11–22. Google Scholar
  4. [4]
    Bäck, T. (1995). “Generalized convergence models for tournament- and (μ, λ)-selections,” in L. J. Eshelman, editor, Proceedings of the 6th International Conference on Genetic Algorithms, 2–8, Morgan Kaufmann Publishers, Los Altos, California.Google Scholar
  5. [5]
    Bäck, T. (1996). Evolutionary Algorithms in theory and practice. Oxford University Press, New York.Google Scholar
  6. [6]
    Bäck, T. and Schütz, M. (1995). “Evolution strategies for mixed-integer optimization of optical multilayer systems,” in [24]. Google Scholar
  7. [7]
    Bäck, T. and Schwefel, H.P. (1993). “An overview of evolutionary algorithms for parameter optimization.” Evolutionary Computation, 1(1), 1–23.Google Scholar
  8. [8]
    Beyer, H.G. (1993). “Toward a theory of evolution strategies: Some asymptotical results for the (1, +λ) theory.” Evolutionary Computation, 1(2), 165–188.Google Scholar
  9. [9]
    Beyer, H.G. (1994). “Toward a theory of evolution strategies, The (μ, λ)-theory.” Evolutionary Computation, 2(4), 381–407.Google Scholar
  10. [10]
    Beyer, H.G. (1995). “Toward a theory of evolution strategies, On the benefit of sex — the (μ/μ, λ)-theory.” Evolutionary Computation, 3(1), 81–111,Google Scholar
  11. [11]
    Beyer, H.G. (1995). “Toward a theory of evolution strategies, Self-adaptation.” Evolutionary Computation, 3(3), 311–347.Google Scholar
  12. [12]
    Cerf, R. (1996). “An asymptotic theory of genetic algorithms,” in J.M. Alliot, E. Lutton, E. Ronald, M. Schoenauer, and D. Snyers, editors, Artificial Evolution vol. 1063 of LNCS. Springer Verlag, New York.Google Scholar
  13. [13]
    Chakraborty, U., Deb, K., and Chakraborty, M. (1996). “Analysis of selection algorithms, A Markov chain approach.” Evolutionary Computation, 4(2), 133–168.Google Scholar
  14. [14]
    Davidor, Y., Schwefel, H. P., and Männer, R. eds. (1994). Proceedings of the Third International Conference on Parallel Problem Solving from Nature (PPSN), Springer-Verlag, New York.Google Scholar
  15. [15]
    Davis, L. (1994). “Adapting Operator Probabilities in Genetic Algorithms,” in [43], 61–69. Google Scholar
  16. [16]
    Davis, L., ed. (1987). Genetic Algorithms and Simulated Annealing, Morgan Kaufmann Publishers, Los Altos, California.Google Scholar
  17. [17]
    Davis, L. and Steenstrup, M. “Genetic Algorithms and Simulated Annealing, An Overview,” in [16], 1–11. Google Scholar
  18. [18]
    Davis, T.E. and Principe, J.C. (1991). “A simulated annealing like convergence theory for simple genetic algorithm,” in R.K. Belew and L.B. Booker, editors, Proceedings of the 4th International Conference on Genetic Algorithms, 174–181, Morgan Kaufmann Publishers, Los Altos, California. Google Scholar
  19. [19]
    Davis T.E. and Principe, J.C. (1993). “A Markov chain framework for the simple genetic algorithm.” Evolutionary Computation, 1(3), 269–292.Google Scholar
  20. [20]
    Eiben, A.E., Raue, P.E., and Ruttkay, Zs. “Genetic Algorithms with Multi-parent Recombination,” in [14], 78–87. Google Scholar
  21. [21]
    Eiben, A.E., Aarts, E.H.L., and Van Hee, K.M. (1991). “Global convergence of genetic algorithms, a Markov chain analysis,” in Hans-Paul Schwefel and Reinhard Männer, editors, Proceedings of the 1st Parallel Problem Solving from Nature, 4–12, Springer Verlag, Berlin.Google Scholar
  22. [22]
    Eshelman, L.J., ed. (1995). Proceedings of the Sixth International Conference on Genetic Algorithms, Morgan Kaufmann Publishers, Los Altos, California.Google Scholar
  23. [23]
    Eshelman, L.J., Caruana, R.A., and Schaffer, J.D. “Biases in the crossover landscape,” in [43], 10–19. Google Scholar
  24. [24]
    Fogel, D.B. (1995). Evolutionary Computation. To-ward a New Philosophy of Machine Intelligence. IEEE Press, Piscataway, New Jersey.Google Scholar
  25. [25]
    Fogel, D.B. and Atmar, W. (1992). Proceedings of the First Annual Conference on Evolutionary Programming, Evolutionary Programming Society, La Jolla, California.Google Scholar
  26. [26]
    Fogel, D.B. and Atmar, W. (1993). Proceedings of the Second Annual Conference on Evolutionary Programming, Evolutionary Programming Society, La Jolla, California.Google Scholar
  27. [27]
    Fogel, D.B., Fogel, L.J., Atmar, W., and Fogel, G.B. “Hierarchic methods of evolutionary programming,” in [24], 175–182. Google Scholar
  28. [28]
    Fogel, D.B. and Stayton, L.C. (1994). “On the effectiveness of crossover in simulated evolutionary optimization.” Bio Systems, 32, 171–182.Google Scholar
  29. [29]
    Fogel, L.J., Owens, A.J., and Walsh, M.J. (1966). Artificial Intelligence through Simulated Evolution. John Wiley, New York.Google Scholar
  30. [30]
    Glover, F. (1977). “Heuristics for Integer Programming Using Surrogate Constraints.” Decision Sciences 8(1), 156–166.Google Scholar
  31. [31]
    Goldberg, D.E. (1989). Genetic algorithms in search, optimization and machine learning. Addison Wesley, Reading, Massachusetts.Google Scholar
  32. [32]
    Goldberg, D.E. and Deb, K. (1991). “A comparative study of selection schemes used in genetic algorithms,” in G. J.E. Rawlins, editor, Foundations of Genetic Algorithms, 69–93, Morgan Kaufmann Publishers, Los Altos, California.Google Scholar
  33. [33]
    Holland, J.H. (1975). Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor.Google Scholar
  34. [34]
    Jones, T. (1995). “Crossover, macromutation and population-based search,” in [22], 73–80. Google Scholar
  35. [35]
    Kinnear, K. E., Jr., ed. (1994). Advances in Genetic Programming, MIT Press, Cambridge, Massachusetts.Google Scholar
  36. [36]
    Koza, J.R. (1994). Genetic Programming, On the Programming of Computers by means of Natural Evolution. MIT Press, Cambridge, Massachusetts.Google Scholar
  37. [37]
    Michalewicz, Z. (1996). Genetic Algorithms + Data Structures = Evolution Programs” Springer Verlag, New York.Google Scholar
  38. [38]
    McDonnell, J. R., Reynolds, R. G., and Fogel, D. B., eds. (1995). Proceedings of the Fourth Annual Conference on Evolutionary Programming. MIT Press, Cambridge, Massachusetts.Google Scholar
  39. [39]
    Miller, B.L. and Goldberg, D.E. (1996). “Genetic algorithms, selection schemes, and the varying effects of noise.” Evolutionary Computation, 4(2), 113–132.Google Scholar
  40. [40]
    Nix, A.E. and Vose, M.D. (1992). “Modeling genetic algorithms with Markov chains.” Annals of Mathematics and Artificial Intelligence, 5(1), 79–88.Google Scholar
  41. [41]
    Radcliffe, N.J. (1991). “Equivalence class analysis of genetic algorithms.” Complex Systems, 5, 183–200.Google Scholar
  42. [42]
    Rechenberg, I. (1973). Evolutionstrategie, Optimierung Technisher Systeme nach Prinzipien des Biologischen Evolution. Fromman-Holzboog Verlag, Stuttgart.Google Scholar
  43. [43]
    Rudolph, G. (1994). “Convergence of non-elitist strategies,” in Z. Michalewicz, J. D. Schaffer, H. P. Schwefel, D. B. Fogel, and H. Kitano, editors, Proceedings of the First IEEE International Conference on Evolutionary Computation, 63–66, IEEE Press, Piscataway, New Jersey.Google Scholar
  44. [44]
    Rudolph, G. (1994). “Convergence analysis of canonical genetic algorithm.” IEEE Transactions on Neural Networks, 5(1), 96–101.Google Scholar
  45. [45]
    Schaffer, J. D., ed. (1989). Proceedings of the Third International Conference on Genetic Algorithms.” Morgan Kaufmann Publishers, Los Altos, California.Google Scholar
  46. [46]
    Schwefel, H.P. (1995). Numerical Optimization of Computer Models (2nd ed.). John Wiley, New York.Google Scholar
  47. [47]
    Syswerda, G. (1991). “A study of reproduction in generational and steady state genetic algorithm,” in G. J.E. Rawlins, editor, Foundations of Genetic Algorithms, 94–101, Morgan Kaufmann Publishers, Los Altos, California.Google Scholar
  48. [48]
    Whitley, D. (1989). “The GENITOR algorithm and selection pressure, Why rank-based allocation of reproductive trials is best,” in J. D. Schaffer, editor, Proceedings of the 3rd International Conference on Genetic Algorithms, 116–121, Morgan Kaufmann Publishers, Los Altos, California.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Zbigniew Michalewicz
    • 1
  • Marc Schoenauer
    • 2
  1. 1.University of North CarolinaCharlotteUSA
  2. 2.Ecole PolytechniqueParisFrance