Abstract
This paper presents a scaling analysis of the ordering messy genetic algorithm (OmeGA), a fast messy genetic algorithm that uses random keys to represent solutions. In experiments with hard permutation problems—so-called ordering deceptive problems—it is shown that the algorithm scales up as O(l 1.4) with the problem length l ranging from 32 to 512. Moreover, the OmeGA performs efficiently with small populations thereby consuming little memory. Since the algorithm is independent of the structure of the building blocks, it outperforms the random key-based simple genetic algorithm (RKGA) for loosely coded problems.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bean, J. C. (1994). Genetic algorithms and random keys for sequencing and optimization. ORSA Journal on Computing, 6(2), 154–160.
Blanton Jr., J. L., & Wainwright, R. L. (1993). Multiple vehicle routing with time and capacity constraints using genetic algorithms. In Proceedings of the Fifth International Conference on Genetic Algorithms (pp. 452–459).
Davis, L. (1985). Job shop scheduling with genetic algorithms. In Proceedings of an International Conference on Genetic Algorithms and Their Applications (pp. 136–140).
Goldberg, D. E. (1987). Simple genetic algorithms and the minimal, deceptive problem. In Genetic Algorithms and Simulated Annealing (Chapter 6, pp. 74–88).
Goldberg, D. E. (1993). Making genetic algorithms fly: A lesson from the Wright Brothers. Advanced Technology for Developers, 2, 1–8.
Goldberg, D. E., Deb, K., Kargupta, H., & Harik, G. (1993). Rapid, accurate optimization of difficult problems using fast messy genetic algorithms. Proceedings of the Fifth International Conference on Genetic Algorithms, 56–64.
Goldberg, D. E., Korb, B., & Deb, K. (1989). Messy genetic algorithms: Motivation, analysis, and first results. Complex Systems, 3(5), 493–530. (Also TCGA Report 89003).
Goldberg, D. E., & Lingle, Jr., R. (1985). Alleles, loci, and the traveling salesman problem. In Proceedings of an International Conference on Genetic Algorithms and Their Applications (pp. 154–159).
Harik, G. R. (1997). Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms. Unpublished doctoral dissertation, University of Michigan, Ann Arbor. Also IlliGAL Report No. 97005.
Kargupta, H., Deb, K., & Goldberg, D. E. (1992). Ordering genetic algorithms and deception. In Parallel Problem Solving from Nature-PPSN II (pp. 47–56).
Knjazew, D., & Goldberg, D. E. (2000). OMEGA-ordering messy ga: Solving permutation problems with the fast messy genetic algorithm and random keys (IlliGAL Report No. 2000004). Urbana, IL.
Louis, S. J., & Rawlins, G. J. E. (1991). Designer genetic algorithms: Genetic algorithms in structure design. In Proceedings of the Fourth International Conference on Genetic Algorithms (pp. 53–60).
Pelikan, M., Goldberg, D. E., & Cantú-Paz, E. (1999). BOA: The Bayesian optimization algorithm. In GECCO-99: Proceedings of 1999 Genetic and Evolutionary Computation Conference, Volume 1 (pp. 525–532).
Thierens, D., & Goldberg, D. E. (1993). Mixing in genetic algorithms. In Proceedings of the Fifth International Conference on Genetic Algorithms (pp. 38–45).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Knjazew, D., Goldberg, D.E. (2000). Large-Scale Permutation Optimization with the Ordering Messy Genetic Algorithm. In: Schoenauer, M., et al. Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, vol 1917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45356-3_62
Download citation
DOI: https://doi.org/10.1007/3-540-45356-3_62
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41056-0
Online ISBN: 978-3-540-45356-7
eBook Packages: Springer Book Archive