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Evolutionary Algorithms and Constraint Satisfaction: Definitions, Survey, Methodology, and Research Directions

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Theoretical Aspects of Evolutionary Computing

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

Abstract

In this tutorial we consider the issue of constraint handling by evolutionary algorithms (EA). We start this study with a categorization of constrained problems and observe that constraint handling is not straightforward in an EA. Namely, the search operators mutation and recombination are ‘blind’ to constraints. Hence, there is no guarantee that if the parents satisfy some constraints the offspring will satisfy them as well. This suggests that the presence of constraints in a problem makes EAs intrinsically unsuited to solve this problem. This should especially hold if there are no objectives but only constraints in the original problem specification — the category of constraint satisfaction problems. A survey of related literature, however, discloses that there are quite a few successful attempts at evolutionary constraint satisfaction. Based on this survey we identify a number of common features in these approaches and arrive at the conclusion that the presence of constraints is not harmful, but rather helpful in that it provides extra information that EAs can utilize. The tutorial is concluded by considering a number of key questions on research methodology and some promising future research directions.

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References

  1. T. Bäck, D. Fogel, and Z. Michalewicz, editors. Handbook of Evolutionary Computation. Institute of Physics, Bristol, and Oxford University Press, New York, 1997.

    MATH  Google Scholar 

  2. T. Bäck, A. E. Eiben, and M. E. Vink. A superior evolutionary algorithm for 3-SAT. In V. William Porto, N. Saravanan, Don Waagen, and A. E. Eiben, editors, Proc. of the 7th Annual Conference on Evolutionary Programming, LNCS 1477, pages 125–136. Springer, Berlin Heidelberg New York, 1998.

    Google Scholar 

  3. J. Bowen and G. Dozier. Solving constraint satisfaction problems using a genetic/systematic search hybrid that realizes when to quit. In Eshelman [19], pages 122–129.

    Google Scholar 

  4. P. Cheeseman, B. Kenefsky, and W. M. Taylor. Where the really hard problems are. In Proc. of the IJCAI-91, pages 331–337. Morgan Kaufmann, San Francisco, 1991.

    Google Scholar 

  5. A. G. Cohn, editor. Proc. of the European Conference on Artificial Intelligence. John Wiley, New York, 1994.

    Google Scholar 

  6. G. Dozier, J. Bowen, and D. Bahler. Solving small and large constraint satisfaction problems using a heuristic-based microgenetic algorithm. In IEEE [24], pages 306– 311.

    Google Scholar 

  7. G. Dozier, J. Bowen, and D. Bahler. Solving randomly generated constraint satisfaction problems using a micro-evolutionary hybrid that evolves a population of hill-climbers. In Proc. of the 2nd IEEE Conference on Evolutionary Computation, pages 614–619. IEEE Press, New York, 1995.

    Google Scholar 

  8. G. Dozier, J. Bowen, and A. Homaifar. Solving constraint satisfaction problems using hybrid evolutionary search. IEEE Transactions on Evolutionary Computation, 2(1):23–33, 1998.

    Article  Google Scholar 

  9. J. Eggermont, A. E. Eiben, and J. I. van Hemert. Adapting the fitness function in GP for data mining. In R. Poli, P. Nordin, W. B. Langdon, and T. C. Fogarty, editors, Genetic Programming, Proc. of EuroGP’99, LNCS 1598, pages 195–204. Springer Verlag, Berlin Heidelberg New York, 1999.

    Google Scholar 

  10. A. E. Eiben and J. K. van der Hauw. Graph coloring with adaptive evolutionary algorithms. Technical Report TR-96–11, Leiden University, August 1996. Also available at http://www..leidenuniv.nl/gusz/graphcol.ps.gz.

    Google Scholar 

  11. A. E. Eiben and J. K. van der Hauw. Solving 3-SAT with adaptive Genetic Algorithms. In IEEE [26], pages 81–86.

    Google Scholar 

  12. A. E. Eiben and J. K. van der Hauw. Adaptive penalties for evolutionary graphcoloring. In J.-K. Hao, E. Lutton, E. Ronald, M. Schoenauer, and D. Snyers, editors, Artificial Evolution’97, LNCS 1363, pages 95–106. Springer-Verlag, Berlin Heidelberg New York, 1998.

    Chapter  Google Scholar 

  13. A. E. Eiben, J. K. van der Hauw, and J. I. van Hemert. Graph coloring with adaptive evolutionary algorithms. Journal of Heuristics, 4(1):25–46, 1998.

    Article  MATH  Google Scholar 

  14. A. E. Eiben, J. I. van Hemert, E. Marchiori, and A. G. Steenbeek. Solving binary constraint satisfaction problems using evolutionary algorithms with an adaptive fitness function. In A. E. Eiben, T. Bäck, M. Schoenauer, and H.-P. Schwefel, editors, Proc. of the 5th Conference on Parallel Problem Solving from Nature, LNCS 1498, pages 196–205. Springer-Verlag, Berlin Heidelberg New York, 1998.

    Chapter  Google Scholar 

  15. A. E. Eiben, P-E. Raué, and Z. Ruttkay. Solving constraint satisfaction problems using genetic algorithms. In IEEE [24], pages 542–547.

    Google Scholar 

  16. A. E. Eiben, P.-E. Raué, and Z. Ruttkay. Constrained problems. In L. Chambers, editor, Practical Handbook of Genetic Algorithms, pages 307–365. CRC Press, 1995.

    Google Scholar 

  17. A. E. Eiben and Z. Ruttkay. Self-adaptivity for constraint satisfaction: learning penalty functions. In IEEE [25], pages 258–261.

    Google Scholar 

  18. A. E. Eiben and Z. Ruttkay. Constraint-satisfaction problems. In Bäck et al. [1], pages C5.7:1–05.7:8.

    Google Scholar 

  19. L. J. Eshelman, editor. Proc. of the 6th International Conference on Genetic Algorithms. Morgan Kaufmann, San Francisco, 1995.

    Google Scholar 

  20. I. Gent, E. Maclntyre, P. Prosser, and T. Walsh. Scaling effects in the CSP phase transition. In 1 st International Conference on Principles and Practice of Constraint Programming, 1995. Also available at http://www.cs.strath.ac.uk/_apes/apepapers.html.

    Google Scholar 

  21. I. Gent and T. Walsh. Phase transitions from real computational problems. In Proc. of the 8th International Symposium on Artificial Intelligence, pages 356–364, 1995.

    Google Scholar 

  22. J.-K. Hao. A clausal representation and its evolutionary procedures for satisfiability problems. In D. W. Pearson, N. C. Steel, and R. F. Albrecht, editors, Proc. of the International Conference on Artificial Neural Networks and Genetic Algorithms, pages 289–292. Springer, Vienna, 1995.

    Chapter  Google Scholar 

  23. T. Hogg and C. Williams. The hardest constraint problems: a double phase transition. Artificial Intelligence, 69:359–377, 1994.

    Article  MATH  Google Scholar 

  24. Proc. 1 st IEEE Conf. on Evolutionary Computation. IEEE Press, New York, 1994.

    Google Scholar 

  25. Proc. 3rd IEEE Conf. on Evolutionary Computation. IEEE Press, New York, 1996.

    Google Scholar 

  26. Proc. 4th IEEE Conf. on Evolutionary Computation. IEEE Press, New York, 1997.

    Google Scholar 

  27. K. A. De Jong and W. M. Spears. Using genetic algorithms to solve NP-complete problems. In Schaffer [43], pages 124–132.

    Google Scholar 

  28. E. Marchiori. Combining constraint processing and genetic algorithms for constraint satisfaction problems. In T. Bäck, editor, Proc. of the 7th International Conference on Genetic Algorithms, pages 330–337. Morgan Kaufmann, San Francisco, 1997.

    Google Scholar 

  29. Z. Michalewicz. Genetic algorithms, numerical optimization, and constraints. In Eshelman [19], pages 151–158.

    Google Scholar 

  30. Z. Michalewicz. A survey of constraint handling techniques in evolutionary computation methods. In J. R. McDonnell, R. G. Reynolds, and D. B. Fogel, editors, Proc. of the 4th Annual Conference on Evolutionary Programming, pages 135–155. MIT Press, Boston, MA, 1995.

    Google Scholar 

  31. Z. Michalewicz. Genetic Algorithms + Data structures = Evolution programs. Springer-Verlag, Berlin Heidelberg New York, 3rd edition, 1996.

    Book  MATH  Google Scholar 

  32. Z. Michalewicz and N. Attia. Evolutionary optimization of constrained problems. In A. V. Sebald and L. J. Fogel, editors, Proc. of the 3rd Annual Conference on Evolutionary Programming, pages 98–108. World Scientific, Singapore, 1994.

    Google Scholar 

  33. Z. Michalewicz and M. Michalewicz. Pro-life versus pro-choice strategies in evolutionary computation techniques. In M. Palaniswami, Y. Attikiouzel, R. J. Marks, D. Fogel, and T. Fukuda, editors, Computational Intelligence: A Dynamic System Perspective, pages 137–151. IEEE Press, New York, 1995.

    Google Scholar 

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

    Article  Google Scholar 

  35. S. Minton, M. D. Johnston, A. Philips, and P. Laird. Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems. Artificial Intelligence, 58 :161–205, 1992.

    Article  MathSciNet  MATH  Google Scholar 

  36. D. Mitchell, B. Selman, and H. J. Levesque. Hard and easy distributions of SAT problems. In Proc. of the AAAI, pages 459–465. San Jose, CA, 1992.

    Google Scholar 

  37. P. Morris. On the density of solutions in equilibrium points for the queens problem. In Proc. of the 10th National Conference on Artificial Intelligence, AAAI-92, pages 428–433. AAAI Press/The MIT Press, Cambridge, MA, 1992.

    Google Scholar 

  38. P. Prosser. Binary constraint satisfaction problems: some are harder than others. In Cohn [5], pages 95–99.

    Google Scholar 

  39. P. Prosser. An empirical study of phase transitions in binary constraint satisfaction problems. Artificial Intelligence, 81:81–109, 1996.

    Article  MathSciNet  Google Scholar 

  40. M. C. Riff-Rojas. Using the knowledge of the constraint network to design an evolutionary algorithm that solves CSP. In IEEE [25], pages 279–284.

    Google Scholar 

  41. J. T. Richardson, M. R. Palmer, G. Liepins, and M. Hilliard. Some guidelines for genetic algorithms with penalty functions. In Schaffer [43], pages 191–197.

    Google Scholar 

  42. M. C. Riff-Rojas. Evolutionary search guided by the constraint network to solve CSP. In IEEE [26], pages 337–348.

    Google Scholar 

  43. J. D. Schaffer, editor. Proc. of the 3rd International Conference on Genetic Algorithms. Morgan Kaufmann, San Francisco, 1989.

    Google Scholar 

  44. B. M. Smith. Phase transition and the mushy region in constraint satisfaction problems. In Cohn [5], pages 100–104.

    Google Scholar 

  45. E. P. K. Tsang. Foundations of Constraint Satisfaction. Academic Press, New York, 1993.

    Google Scholar 

  46. P. van Hentenryck, V. Saraswat, and Y. Deville. Constraint processing in cc(fd). In A. Podelski, editor, Constraint programming: basics and trends. Springer-Verlag, Berlin Heidelberg New York, 1995.

    Google Scholar 

  47. D. Whitley. Permutations. In T. Bäck et al. [1], pages C3.2:5 — C3.2:8. Chapter C3.2, Mutation.

    Google Scholar 

  48. D. Whitley. Permutations. In T. Bäck et al. [1], pages C3.3:14 — C3.3:20. Chapter C3.3, Recombination.

    Google Scholar 

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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. Ecole Polytechnique, CMAP, 91128, Palaiseau Cedex, France

    Leila Kallel

  2. Departement Wiskunde & Informatica, Universiteit Antwerpen (RUCA), Groenenborgerlaan 171, 2020, Antwerpen, Belgium

    Bart Naudts

  3. Department of Electronics and Computer Science Highfield, University of Southampton, SO17 1BJ, Southampton, UK

    Alex Rogers

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Eiben, A.E. (2001). Evolutionary Algorithms and Constraint Satisfaction: Definitions, Survey, Methodology, and Research Directions. In: Kallel, L., Naudts, B., Rogers, A. (eds) Theoretical Aspects of Evolutionary Computing. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04448-3_2

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  • DOI: https://doi.org/10.1007/978-3-662-04448-3_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-08676-2

  • Online ISBN: 978-3-662-04448-3

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