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Further experimentations on the scalability of the GEMGA

  • Hillol Kargupta
  • Sanghamitra Bandyopadhyay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1498)

Abstract

This paper reports the recent developments of the Gene Expression Messy Genetic Algorithm (GEMGA) research. It presents extensive experimental results for large problems with massive multi-modality, non-uniform scaling, and overlapping sub-problems. All the experimental results corroborate the linear time performance of the GEMGA for a wide range of problems, that can be decomposed into smaller overlapping and non-overlapping sub-problems in the chosen representation. These results further support the scalable performance of the GEMGA.

Keywords

Problem Size Linear Time Performance Fitness Landscape Recombination Operator Extensive Experimental Result 
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.

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References

  1. 1.
    D. H. Ackley. A connectionist machine for genetic hill climbing. Kluwer Academic, Boston, 1987.Google Scholar
  2. 2.
    S. Baluja and S. Davies. Using optimal dependency-trees for combinatorial optimization: Learning the structure of the search space. Technical Report CMU-CS-97-107, Departement of Computer Science, Carnegie Mellon University, Pittsburgh, 1997.Google Scholar
  3. 3.
    S. Bandyopadhyay, H. Kargupta, and G. Wang. Revisiting the GEMGA: Scalable evolutionary optimization through linkage learning. In Proceedings of the IEEE International Conference on Evolutionary Computation, pages 603–608. IEEE Press, 1998.Google Scholar
  4. 4.
    K. Deb. Binary and floating-point function optimization using messy genetic algorithms. IlliGAL Report no. 91004 and doctoral dissertation, unversity of alabama, tuscaloosa, University of Illinois at Urbana-Champaign, Illinois Genetic Algorithms Laboratory, Urbana, 1991.Google Scholar
  5. 5.
    D. E. Goldberg, K. Deb, H. Kargupta, and G. Harik. Rapid, accurate optimizaiton of difficult problems using fast messy genetic algorithms. Proceedings of the Fifth International Conference on Genetic Algorithms, pages 56–64, 1993.Google Scholar
  6. 6.
    D. E. Goldberg, B. Korb, and K. Deb. Messy genetic algorithms: Motivation, analysis, and first results. Complex Systems, 3(5):493–530, 1989. (Also TCGA Report 89003).zbMATHMathSciNetGoogle Scholar
  7. 7.
    G. Harik. Learning Linkage to Efficiently Solve Problems of Bounded Difficulty Using Genetic Algorithms. PhD thesis, Department of Computer Science, University of Michigan, Ann Arbor, 1997.Google Scholar
  8. 8.
    J. H. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, 1975.Google Scholar
  9. 9.
    P. Indyk, R. Motwani, P. Raghavan, and S. Vempala. Locality-presering hashing in multidimensional spaces. In http://www.cs.stanford.edu/indyk, 1997.Google Scholar
  10. 10.
    H. Kargupta. Computational processes of evolution: The SEARCH perspective. Presented in SIAM Annual Meeting, 1996 as the winner of the 1996 SIAM Annual Best Student Paper Prize, July 1996.Google Scholar
  11. 11.
    H. Kargupta. The gene expression messy genetic algorithm. In Proceedings of the IEEE International Conference on Evolutionary Computation, pages 814–819. IEEE Press, 1996.Google Scholar
  12. 12.
    H. Kargupta. Gene Expression: The Missing Link Of Evolutionary Computation. In C. Poloni D. Quagliarella, J. Periaux and G. Winter, editors, Genetic Algorithms in Engineering and Computer Science., page Chapter 4. John Wiley & Sons Ltd., 1997.Google Scholar
  13. 13.
    H. Kargupta, E. Riva Sanseverino, E. Johnson, and S. Agrawal. The genetic algorithms, linkage learning, and scalable data mining. 1998.Google Scholar
  14. 14.
    N. Linial and O. Sasson. Non-expansive hashing. In Journal Of ACM, pages 509–518, 1996.Google Scholar
  15. 15.
    H. Muhlenbein and G. Paab. Prom recombination of genes to the estimation of distributions i. binary parameters. In Parallel Problem Solving from Nature — PPSN IV, pages 178–187, Berlin, 1996. Springer.Google Scholar
  16. 16.
    H. Mühlenbein and A. O. Rodriguez. Schemata, distributions and graphical models in evolutionary optimization. Personal Communication., December 1997.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Hillol Kargupta
    • 1
  • Sanghamitra Bandyopadhyay
    • 2
  1. 1.School of Electrical Engineering and Computer ScienceWashington State UniversityPullmanUSA
  2. 2.Machine Intelligence UnitIndian Statistical InstituteCalcuttaIndia

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