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Link to original content: https://doi.org/10.1007/3-540-45105-6_87
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An Adaptive Penalty Scheme for Steady-State Genetic Algorithms

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Genetic and Evolutionary Computation — GECCO 2003 (GECCO 2003)

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Abstract

A parameter-less adaptive penalty scheme for steady-state genetic algorithms applied to constrained optimization problems is proposed. For each constraint, a penalty parameter is adaptively computed along the run according to information extracted from the current population such as the existence of feasible individuals and the level of violation of each constraint. Using real coding, rank-based selection, and operators available in the literature, very good results are obtained.

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References

  1. M. Schoenauer and Z. Michalewicz. Evolutionary computation at the edge of feasibility. In Parallel Problem Solving from Nature-PPSN IV, volume 1141, pages 245–254. Springer-Verlag, 1996. LNCS.

    Article  Google Scholar 

  2. S. Koziel and Z. Michalewicz. Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization. Evolutionary Computation, 7(1):19–44, 1999.

    Article  Google Scholar 

  3. G.E. Liepins and W.D. Potter. A genetic algorithm approach to multiple-fault diagnosis. In Lawrence Davis, editor, Handbook of Genetic Algorithms, chapter 17, pages 237–250. Van Nostrand Reinhold, New York, New York, 1991.

    Google Scholar 

  4. D. Orvosh and L. Davis. Using a genetic algorithm to optimize problems with feasibility contraints. In Proc. of the First IEEE Conf. on Evolutionary Computation, pages 548–553, 1994.

    Google Scholar 

  5. H. Adeli and N-T. Cheng. Augmented lagrangian genetic algorithm for structural optimization. Journal of Aerospace Engineering, 7(1):104–118, January 1994.

    Article  Google Scholar 

  6. H.J.C. Barbosa. A coevolutionary genetic algorithm for constrained optimization problems. In Proc. of the Congress on Evolutionary Computation, pages 1605–1611, Washington, DC, USA, 1999.

    Google Scholar 

  7. P.D. Surry and N.J. Radcliffe. The COMOGA method: Constrained optimisation by multiobjective genetic algorithms. Control and Cybernetics, 26(3), 1997.

    Google Scholar 

  8. T.P. Runarsson and X. Yao. Stochastic ranking for constrained evolutionary optimization. IEEE Trans. on Evolutionary Computation, 4(3):284–294, 2000.

    Article  Google Scholar 

  9. A.H.C. van Kampen, C.S. Strom, and L.M.C. Buydens. Lethalization, penalty and repair functions for constraint handling in the genetic algorithm methodology. Chemometrics and Intelligent Laboratory Systems, 34:55–68, 1996.

    Article  Google Scholar 

  10. Z. Michalewicz and M. Schoenauer. Evolutionary algorithms for constrained parameter optimization problems. Evolutionary Computation, 4(1):1–32, 1996.

    Article  Google Scholar 

  11. R. Hinterding and Z. Michalewicz. Your brains and my beauty: Parent matching for constrained optimization. In Proc. of the Fifty Int. Conf. on Evolutionary Computation, pages 810–815, Alaska, May 4–9 1998.

    Google Scholar 

  12. S. Koziel and Z. Michalewicz. A decoder-based evolutionary algorithm for constrained optimization problems. In Proc. of the Fifth Parallel Problem Solving from Nature. Springer-Verlag, 1998. Lecture Notes in Computer Science.

    Google Scholar 

  13. J.-H. Kim and H. Myung. Evolutionary programming techniques for constrained optimization problems. IEEE Trans. on Evolutionary Computation, 2(1):129–140, 1997.

    MathSciNet  Google Scholar 

  14. H.J.C. Barbosa and A.C.C. Lemonge. An adaptive penalty scheme in genetic algorithms for constrained optimization problems. In Proc. of the Genetic and Evolutionary Computation Conference, pages 287–294. Morgan Kaufmann Publishers, 2002.

    Google Scholar 

  15. S.E. Carlson and R. Shonkwiler. Annealing a genetic algorithm over constraints. In Proc. of the IEEE Int. Conf. on Systems, Man and Cybernetics, pages 3931–3936, 1998.

    Google Scholar 

  16. D. Powell and M.M. Skolnick. Using genetic algorithms in engineering design optimization with non-linear constraints. In Proc. of the Fifth Int. Conf. on Genetic Algorithms, pages 424–430. Morgan Kaufmann, 1993.

    Google Scholar 

  17. K. Deb. An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186(2–4):311–338, June 2000.

    Article  MATH  Google Scholar 

  18. Z. Michalewicz. A survey of constraint handling techniques in evolutionary computation. In Proc. of the 4th Int. Conf. on Evolutionary Programming, pages 135–155, Cambridge, MA, 1995. MIT Press.

    Google Scholar 

  19. Z. Michalewicz, D. Dasgupta, R.G. Le Riche, and M. Schoenauer. Evolutionary algorithms for constrained engineering problems. Computers & Industrial Engineering Journal, 30(2):851–870, 1996.

    Article  Google Scholar 

  20. R.G. Le Riche, C. Knopf-Lenoir, and R.T. Haftka. A segregated genetic algorithm for constrained structural optimization. In Proc. of the Sixth Int. Conf. on Genetic Algorithms, pages 558–565, 1995.

    Google Scholar 

  21. H. Homaifar, S.H.-Y. Lai, and X. Qi. Constrained optimization via genetic algorithms. Simulation, 62(4):242–254, 1994.

    Article  Google Scholar 

  22. J.A Joines and C.R. Houck. On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GAs. In Proc. of the First IEEE Int. Conf. on Evolutionary Computation, pages 579–584, June 19–23 1994.

    Google Scholar 

  23. J.C. Bean and A.B. Alouane. A dual genetic algorithm for bounded integer programs. Dept. of Industrial and Operations Engineering, The University of Michigan, Tech. Rep. 92-53 1992.

    Google Scholar 

  24. D.W. Coit, A.E. Smith, and D.M. Tate. Adaptive penalty methods for genetic optimization of constrained combinatorial problems. INFORMS Journal on Computing, 6(2):173–182, 1996.

    Article  Google Scholar 

  25. M. Schoenauer and S. Xanthakis. Constrained GA optimization. In Proc. of the Fifth Int. Conf. on Genetic Algorithms, pages 573–580. Morgan Kaufmann Publishers, 1993.

    Google Scholar 

  26. S. Ben Hamida and M. Schoenauer. ASCHEA: new results using adaptive segregational constraint handling. In Proc. of the 2002 Congress on Evolutionary Computation, volume 1, pages 884–889, May 2002.

    Article  Google Scholar 

  27. J.A. Wright and R. Farmani. Genetic algorithms: A fitness formulation for constrained minimization. In GECCO 2001: Proc. of the Genetic and Evolutionary Computation Conference, pages 725–732. Morgan Kaufmann, 2001.

    Google Scholar 

  28. A.E. Eiben and J. I. van Hemert. Saw-ing EAs: adapting the fitness function for solving constrained problems. In D. Corne, M. Dorigo, and F. Glover, editors, New ideas in optimization, chapter 26, pages 389–402. McGraw-Hill, London, 1999.

    Google Scholar 

  29. Z. Michalewicz and D.B. Fogel. How to Solve It: Modern Heuristics. Springer-Verlag, 1999.

    Google Scholar 

  30. Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, New York, 1992.

    MATH  Google Scholar 

  31. H. Muhlenbein, M. Schomisch, and J. Born. The parallel genetic algorithm as function optimizer. Parallel Computing, 17(6–7):619–632, Sep 1991.

    Article  Google Scholar 

  32. K. Deb and H.G. Beyer. Self-adaptive genetic algorithms with simulated binary crossover. Evolutionary Computation Journal, 9(2):197–221, 2001.

    Article  Google Scholar 

  33. S.B. Hamida and M. Schoenauer. An adaptive algorithm for constrained optimization problems. In PPSN VI — LNCS, volume 1917, pages 529–538. Springer-Verlag, 2000.

    Google Scholar 

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Author information

Authors and Affiliations

  1. LNCC/MCT, Rua Getulio Vargas 333, 25651 070, Petropolis RJ, Brazil

    Helio J. C. Barbosa

  2. Depto. de Estruturas, Faculdade de Engenharia, Universidade Federal de Juiz de Fora, 36036 330, Juiz de Fora MG, Brazil

    Afonso C. C. Lemonge

Authors
  1. Helio J. C. Barbosa
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  2. Afonso C. C. Lemonge
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Editor information

Editors and Affiliations

  1. Lawrence Livermore National Laboratory, Center for Applied Scientific Computing (CASC), 7000 East Avenue, L-561, Livermore, CA, 94550, USA

    Erick Cantú-Paz

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Barbosa, H.J.C., Lemonge, A.C.C. (2003). An Adaptive Penalty Scheme for Steady-State Genetic Algorithms. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45105-6_87

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  • DOI: https://doi.org/10.1007/3-540-45105-6_87

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40602-0

  • Online ISBN: 978-3-540-45105-1

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