Why is problem-dependent and high-level representation scheme better in a genetic algorithm?

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Why is problem-dependent and high-level representation scheme better in a genetic algorithm?

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Symposium on Applied Computing archive
Proceedings of the 1997 ACM symposium on Applied computing table of contents

San Jose, California, United States

Pages: 239 - 246

Year of Publication: 1997

ISBN:0-89791-850-9

Authors

Sam Chung	Regenstrief Institute for Health Care, Indiana University Medical Center, 1001 West 10th St., Indianapolis, IN
Rafael Perez	Dept. of Computer Science & Engineering, University of South Florida, 4202 East Fowler Ave., ENB 118, Tampa, FL

Sponsors

SIGCUE: ACM Special Interest Group on Computer Uses In Education
SIGADA: ACM Special Interest Group on Ada Programming Language
SIGAPP: ACM Special Interest Group on Applied Computing
SIGBIO: ACM Special Interest Group on Biomedical Computing

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ACM New York, NY, USA

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Weon Sam Chung, An analysis of the effects of different representation schemes on genetic algorithms, University of South Florida, Tampa, FL, 1996
	2	Thomas Elder Davis, Toward an extrapolation of the simulated annealing convergence theory onto the simple genetic algorithm144438, University of Florida, Gainesville, FL, 1992
	3	De Jong, K. A. (1975). An analysis of the behavior of a class of genetic adaptive systems. Dissertation Abstracts International 36(10), 5140B. (University Microfilms No. 76-9381).
	4	David E. Goldberg , Philip Segrest, Finite Markov chain analysis of genetic algorithms, Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application, p.1-8, October 1987, Cambridge, Massachusetts, United States
	5	David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, 1989
	6	John H. Holland, Adaptation in natural and artificial systems, MIT Press, Cambridge, MA, 1992
	7	Kemeny, J. C. and Snell, J. L. (1967). Finite Markov Chains. Princeton, NJ: D. Van Nostrand Company, Inc.
	8	Nix, A. E. (1991). Comparing finite and inf'mite population models of a genetic algorithm using the minimal deceptive problem. Master Thesis. Knoxville, TA: University of Tennessee
	9	Snell, J. L. (1988). Introduction to Probability. New York, NY: The Random House.