Applying Genetic Algorithms to Real-World Problems

  • Emanuel Falkenauer
Part of the The IMA Volumes in Mathematics and its Applications book series (IMA, volume 111)

Abstract

This paper outlines what the author perceives as crucial ingredients of a successful application of Genetic Algorithms (GAs) to real-world combinatorial problems. First, the importance of the Schema Theorem is stressed, pointing to crossover as the most potent force in a GA. Second, the importance of an encoding and operators adapted to the problem being solved is demonstrated, with two implications: the importance of the binary alphabet has been largely overstated in the past (in many problems it is not only unwarranted, it is detrimental), and practical GAS must be built to solve problems (i.e., sets of instances) rather than (arbitrary) functions.

The benefits of the above guidelines are illustrated by the Grouping GA (GGA), applied to three different grouping problems, namely Bin Packing and its variety Line Balancing, Equal Piles and Economies of Scale. The first application suggests a superiority of crossover-based search over a classic Branch & Bound, the second shows the superiority of the GGA over standard GAs applied to grouping problems, and the third illustrates the kind of industrial applications GAS can be called upon to solve.

Keywords

Recombination 

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References

  1. [1]
    E. Falkenauer, The grouping genetic algorithms - widening the scope of the gas, JORBEL - Belgian Journal of Operations Research, Statistics and Computer Science, 33 (1993), pp. 79–102.MATHGoogle Scholar
  2. [2]
    E. Falkenauer, A new representation and operators for GAs applied to grouping problems, Evolutionary Computation, 2 (1994), pp. 123–144.CrossRefGoogle Scholar
  3. [3]
    E. Falkenauer, Solving equal piles with a grouping genetic algorithm, in Proceedings of the Sixth International Conference on Genetic Algorithms (IGGA95), L. J. Eshelman, ed., San Mateo, CA, July 1995, University of Pittsburg (Pennsylvania), Morgan Kaufman Publishers, pp. 492–497.Google Scholar
  4. [4]
    E. Falkenauer, A hybrid grouping genetic algorithm for bin packing,Journal of Heuristics, 2 (1996), pp. 5–30.CrossRefGoogle Scholar
  5. [5]
    E. Falkenauer and A. Delchambre, A genetic algorithm for bin packing and line balancing, in Proceedings of the 1992 IEEE International Conference on Robotics and Automation, Los Alamitos, CA, May 1992, IEEE Computer Society Press, pp. 1186–1192.Google Scholar
  6. [6]
    D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison—Wesley Publishing Company Inc., 1989.MATHGoogle Scholar
  7. [7]
    J. H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, 1975.Google Scholar
  8. [8]
    D. R. Jones and M. A. Beltramo, Solving partitioning problems with genetic algorithms, in Proceedings of the Fourth International Conference on Genetic Algorithms, R. K. Belew and L. B. Booker, eds., San Mateo, CA, July 1991, University of California, Morgan Kaufmann Publishers.Google Scholar
  9. [9]
    S. Martello and P. Toth, Lower bounds and reduction procedures for the bin packing problem, Discrete Applied Mathematics, 22 (1990), pp. 59–70.MathSciNetCrossRefGoogle Scholar
  10. [10]
    H. Muhlenbein, Parallel genetic algorithms in combinatorial optimization,in Computer Science and Operations Research - New Developments in Their Interfaces, O. Balci, R. Sharda, and S. A. Zenios, eds., Pergamon Press, 1992, pp. 441–453.Google Scholar
  11. [11]
    N. Radcliffe and P. Surry, Fundamental limitations on search algorithms: Evolutionary computing in perspective, in LNCS1000, Springer-Verlag, 1995.Google Scholar
  12. [12]
    G. Syswerda, Uniform crossover in genetic algorithms,in Proceedings of the Third International Conference on Genetic Algorithms, D. J. Shaffer, ed., San Mateo, CA, June 1989, George Mason University, Morgan Kaufman Publishers, pp. 2–9.Google Scholar
  13. [13]
    G. von Laszewski, Intelligent structural operators for the k-way graph partitioning problem, in Proceedings of the Fourth International Conference on Genetic Algorithms, R. K. Belew and L. B. Booker, eds., San Mateo, CA, July 1991, University of California, Morgan Kaufmann Publishers.Google Scholar
  14. [14]
    D. H. Wolpert and W. G. Macready, No free lunch theorems for search, Tech. Rep. SFI–TR–95–02–010, The Santa Fe Institute, Santa Fee, 1995.Google Scholar

Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Emanuel Falkenauer
    • 1
  1. 1.Department of Applied MechanicsBrussels University (ULB)BrusselsBelgium

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