Applying Genetic Algorithms to Real-World Problems
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.
KeywordsGenetic Algorithm Tabu Search Crossover Operator Line Balance Standard Crossover
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- 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
- 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
- J. H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, 1975.Google Scholar
- 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
- 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
- N. Radcliffe and P. Surry, Fundamental limitations on search algorithms: Evolutionary computing in perspective, in LNCS1000, Springer-Verlag, 1995.Google Scholar
- 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
- 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
- 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