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Fast Evolutionary Algorithms

  • Chapter
Advances in Evolutionary Computing

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

Abstract

This chapter discusses a number of recent results in evolutionary optimization. In particular, we show that the search step size of a variation operator plays a vital role in its efficient search of a landscape. We have derived the optimal search step size of mutation operators in evolutionary optimization. Based on this theoretical analysis, we have developed several new evolutionary algorithms which outperform existing evolutionary algorithms significantly on many benchmark functions.

Most of the existing work in evolutionary optimization concentrates on different variation (i.e., search) operators, such as crossover and mutation. However, there may be a better way to solve a complex problem by transforming it into a simpler one first and then solving it. The key issue here is how to approximate the problem without changing the nature of the problem (i.e., the optima we wish to find). This chapter will present the latest results on landscape approximation and hybrid evolutionary algorithms.

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References

  1. Yao, X. (1996) An overview of evolutionary computation. Chinese Journal of Advanced Software Research (Allerton Press, Inc., New York, NY 10011), 3, 12–29

    Google Scholar 

  2. Kirkpatrick, S., Gelatt, CD., Vecchi, M.P. (1983) Optimization by simulated annealing. Science, 220, 671–680

    Article  MathSciNet  MATH  Google Scholar 

  3. Szu, H.H., Hartley, R.L. (1987) Fast simulated annealing. Physics Letters A, 122, 157–162

    Article  Google Scholar 

  4. Ingber, L. (1989) Very fast simulated re-annealing. Mathl. Comput. Modelling, 12, 967–973

    Article  MathSciNet  MATH  Google Scholar 

  5. Yao, X. (1995) A new simulated annealing algorithm. Int. J. of Computer Math., 56, 161–168

    Article  MATH  Google Scholar 

  6. Grefenstette, J.J. (1987) Incorporating problem specific knowledge into genetic algorithms. In L. Davis, editor, Genetic Algorithms and Simulated Annealing, chapter 4, 42–60. Morgan Kaufmann, San Mateo, CA

    Google Scholar 

  7. Fogel, D.B. (1991) System Identification Through Simulated Evolution: A Machine Learning Approach to Modeling. Ginn Press, Needham Heights, MA

    Google Scholar 

  8. Fogel, D.B. (1992) Evolving Artificial Intelligence. PhD thesis, University of California, San Diego, CA

    Google Scholar 

  9. Fogel, D.B. (1993) Applying evolutionary programming to selected traveling salesman problems. Cybernetics and Systems, 24, 27–36

    Article  MathSciNet  Google Scholar 

  10. Yao, X., Liu, Y., Lin, G. (1999) Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 3, 82–102

    Article  Google Scholar 

  11. Fogel, D.B. (1994) An introduction to simulated evolutionary optimisation. IEEE Trans, on Neural Networks, 5, 3–14

    Article  Google Scholar 

  12. Bäck, T., Schwefel, H.P. (1993) An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation, 1, 1–23

    Article  Google Scholar 

  13. Fogel, D.B. (1995) Evolutionary Computation: Towards a New Philosophy of Machine Intelligence. IEEE Press, New York, NY

    Google Scholar 

  14. Gehlhaar, D.K., Fogel, D.B. (1996) Tuning evolutionary programming for conformationally flexible molecular docking. In L.J. Fogel, P.J. Angeline, and T. Bäck, editors, Evolutionary Programming V: Proc. of the Fifth Annual Conference on Evolutionary Programming, 419–429. MIT Press, Cambridge, MA

    Google Scholar 

  15. Feller, W. (1971) An Introduction to Probability Theory and Its Applications, volume 2. John Wiley& Sons, 2nd edition

    Google Scholar 

  16. Yao, X., Liu, Y. (1996) Fast evolutionary programming. In L. J. Fogel, P.J. Angeline, and T. Back, editors, Evolutionary Programming V: Proc. of the Fifth Annual Conference on Evolutionary Programming, 451–460, MIT Press, Cambridge, MA.

    Google Scholar 

  17. Törn, A., Zilinskas, A. (1989) Global Optimisation. Lecture Notes in Computer Science, 350. Springer-Verlag, Berlin.

    Google Scholar 

  18. Schwefel, H.P. (1995) Evolution and Optimum Seeking. John Wiley& Sons, New York

    Google Scholar 

  19. Wolpert, D.H., Macready, W.G. (1995) No free lunch theorems for search. Technical Report SFI-TR-95-02-010, Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA

    Google Scholar 

  20. Wolpert, D.H., Macready, W.G. (1997) No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1, 67–82

    Article  Google Scholar 

  21. Devroye, L. (1986) Non-Uniform Random Variate Generation. Springer-Verlag, New York, NY

    MATH  Google Scholar 

  22. Hunt, R.A. (1986) Calculus with Analytic Geometry. Harper& Row, New York, NY

    Google Scholar 

  23. Yao, X. (1994) Introduction. Informatica (Special Issue on Evolutionary Computation), 18, 375–376

    MATH  Google Scholar 

  24. Chellapilla, K. (1998) Combining mutation operators in evolutionary programming. IEEE Transactions on Evolutionary Computation, 2, 91–96

    Article  Google Scholar 

  25. Törn, A.A. (1978) A search-clustering approach to global optimization. In L.C.W. Dixon and G.P. Szegö, editors, Towards Global Optimization 2, 49–62, North-Holland, Amsterdam

    Google Scholar 

  26. Rinnooy Kan, A.H.G., Timmer, G.T. (1987) Stochastic global optimization methods part II: Multi level methods. Mathematical Programming, 39, 57–78

    Article  MathSciNet  MATH  Google Scholar 

  27. Dixon, L.C.W., Szegö, G.P. (1978) The global optimization problem: An introduction. In L.C.W. Dixon and G.P. Szegö, editors, Towards Global Optimization 2, 1–15, Amsterdam. North-Holland

    Google Scholar 

  28. Ali, M.M., Storey, C., Tör n, A. (1997) Application of stochastic global optimization algorithms to practical problems. Journal of Optimization and Application, 95, 545–563

    Article  MATH  Google Scholar 

  29. Whitley, D., Gordon, V.S., Mathias, K. (1994) Lamarkian evolution, the baldwin effect and function optimization. In Y. Davidor, H.-P. Schwefel, and R. Männer, editors, Parallel Problem Solving from Nature-PPSN III, Lecture Notes in Computer Science, 866, 6–15, Springer-Verlag, Berlin.

    Google Scholar 

  30. Hart, W.E., Belew, R.K. (1996) Optimization with genetic algorithm hybrids that use local search. In R.K. Belew and M. Mitchell, editors, Adaptive Individuals in Evolving Populations: Models and Algorithms, volume 26 of SFI Studies in the Sciences of Complexity, 483-496, Addison-Wesley, Reading, MA.

    Google Scholar 

  31. Baldwin, J.M. (1896) A new factor in evolution. American Naturalist, 30, 441–451

    Article  Google Scholar 

  32. Hinton, G.E., Nolan, S.J. (1987) How learning can guide evolution. Complex Systems, 1, 495–502

    MATH  Google Scholar 

  33. Powell, M.J.D. (1964) An efficient method for finding the minimum of a function of several variables without calculating derivatives. The Computer Journal, 7, 155–162

    Article  MathSciNet  MATH  Google Scholar 

  34. Neider, J.A., Mead, R. (1965) A simplex method for function minimization. The Computer Journal, 7, 308–313

    Article  Google Scholar 

  35. Powell, M.J.D. (1994) A direct search optimization method that models the objective and constraint functions by linear interpolation. In S. Gomez and J.-P. Hennart, editors, Advances in Optimization and Numerical Analysis, Proceedings of the Sixth Workshop on Optimization and Numerical Analysis, Oaxaca, Mexico, 275, 51–67, Kluwer Academic, Dordrechth, NL.

    Chapter  Google Scholar 

  36. H.-M. Voigt and J.M. Lange. Local evolutionary search enhancement by random memorizing. In Proceedings of the 1998 IEEE International Conference on Evolutionary Computation (ICEC ’98), 547–552, Piscataway, NJ, 1998. IEEE Press.

    Google Scholar 

  37. Winfield, D. (1973) Function minimization by interpolation in a data table. Journal of the Institute of Mathematics and its Applications, 12, 339–347

    Article  MathSciNet  MATH  Google Scholar 

  38. Powell, M.J.D. (1994) A direct search optimization method that models the objective by quadratic interpolation. Presentation at the 5th Stockholm Optimization Days

    Google Scholar 

  39. Conn, A.R., Toint, Ph.L. (1996) An algorithm using quadratic interpolation for unconstrained derivative free optimization. In G. Di Pillo and F. Gianessi, editors, Nonlinear Optimization and Applications, 27–47, Plenum Publishing, New York.

    Google Scholar 

  40. Liang, K.H., Yao, X., Newton, C. (1999) Combining landscape approximation and local search in global optimization. In Proceedings of the 1999 Congress on Evolutionary Computation, 2, 1514–1520, IEEE Press, Piscataway, NJ.

    Google Scholar 

  41. Schaffer, J. D., Caruana, R. A., Eshelman, L.J., Das, R. (1989) A study of control parameters affecting online performance of genetic algorithms for function optimization. In J.D. Schaffer, editor, Proceedings of the third International Conference on Genetic Algorithms (ICGA’ 89)‚ 51–60, Morgan Kaufmann, San Mateo, CA.

    Google Scholar 

  42. Yao, X., Lin, G., Liu, Y. (1997) An analysis of evolutionary algorithms based on neighbourhood and step sizes. In P.J. Angeline, R.G. Reynolds, J.R. McDonnell, and R. Eberhart, editors, Evolutionary Programming VI: Proc. of the Sixth Annual Conference on Evolutionary Programming, Lecture Notes in Computer Science, 1213, 297–307, Springer, Berlin.

    Chapter  Google Scholar 

  43. Back, Th., Eiben, A. E. (1999) Generalizations of intermediate recombination in evolution strategies. In Proceedings of the 1999 Congress on Evolutionary Computation, 2, 1566–1573, IEEE Press, Piscataway, NJ.

    Google Scholar 

  44. Born, J. (1996) An evolution strategy with adaptation of the step sizes by a variance function. In H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel, editors, Parallel Problem Solving from Nature (PPSN) IV, Lecture Notes in Computer Science, 1141, 388–397, Springer-Verlag, Berlin.

    Google Scholar 

  45. Kappler, C. (1996) Are evolutionary algorithms improved by large mutations? In H.-M. Voigt, W. Ebeling, I. Rechenberg, and H.-P. Schwefel, editors, Parallel Problem Solving from Nature (PPSN) IV, Lecture Notes in Computer Science, 1141, 346–355, Springer-Verlag, Berlin.

    Chapter  Google Scholar 

  46. Yao, X., Liu, Y. (1997) Fast evolution strategies. Control and Cybernetics, 26, 467–496

    MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Science, The University of Birmingham Edgbaston, Birmingham, B15 2TT, UK

    Xin Yao

  2. Computer Science Division, Evolvable Systems Laboratory, mbox 1501 Electrotechnical Laboratory, 1-1-4 Umezono, Tsukuba, Ibaraki, 305-8568, Japan

    Yong Liu

  3. The University of New South Wales Australian Defence Force Academy, School of Computer Science University College, Canberra, ACT, Australia, 2600

    Ko-Hsin Liang & Guangming Lin

Authors
  1. Xin Yao
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  2. Yong Liu
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  3. Ko-Hsin Liang
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  4. Guangming Lin
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Editor information

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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Yao, X., Liu, Y., Liang, KH., Lin, G. (2003). Fast Evolutionary Algorithms. 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_2

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  • DOI: https://doi.org/10.1007/978-3-642-18965-4_2

  • 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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