Abstract
Genetic algorithms (GAs) have been fairly successful in a diverse range of optimization problems, providing an efficient and robust way for guiding a search even in a complex system and in the absence of domain knowledge. In this paper, two types of genetic algorithms, real-coded and binary-coded, are examined for function optimization and applied to the optimization of a flood control reservoir model. The results show that both genetic algorithms are more efficient and robust than the random search method, with the real-coded GA performing better in terms of efficiency and precision than the binary-coded GA.
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References
Antonisses, H. J.: 1989, A new interpretation of schema notation that overturns the binary encoding constraint, Proc. Third. International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, pp. 86-91.
Davis, T. E.: 1991b, Toward an extrapolation of the simulated annealing convergence theory onto the simple genetic algorithm, Ph. D. Thesis, University of Florida.
Davis, L.: 1991a, Hybridization and numerical representation, in L. Davis (ed.), The Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York, pp. 61-71.
DeJong, K. A.: 1975, Analysis of the behavior of a class of genetic adaptive Systems, Ph. D. Dissertation, Department of Computer and Communications Sciences, University of Michigan, Ann Arbor, MI.
Eshelman, Larry J. and Schaffer, J. David: 1992, Real-coded genetic algorithms and interval-schemata, Proc. Fifth International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, pp. 187-202.
Goldberg, D. E.: 1985, Optimal initial population size for binary-coded genetic algorithms, TCGA Report No. 85001, University of Alabama, Tuscaloosa.
Goldberg, D. E.: 1989, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA.
Grefenstette, John J.: 1986, Optimization of control parameters for genetic algorithms IEEE Trans. System, Man, Cybernet., SMC16(1) 122-128.
Holland, J. H.: 1975, Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbor, MI.
Kojiri, T. and Ikebuchi, S.: 1988, Real-time operation of dam reservoir by using fuzzy inference theory, 6th Congress Asian and Pacific Regional Division International Association for Hydraulic Research, Kyoto, Japan, 20-22 July, pp. 437-445.
McKinney, D. C. and M.-D. Lin: 1994, Groundwater optimization using genetic algorithms, Water Resour. Res. 30(6), 1897.
Nix, A. and Vose, M. D.: 1991, Modeling genetic algorithm with Markov chains, Ann. Math. Artificial Intell.
Oliveira, R. and Loucks, D. P.: 1997, Operating rules for multireservoir systems, Water Resour. Res. 33(4), 839.
Radcliffe, N. J.: 1990, Genetic neural networks on MIMD computers, Ph. D., Dissertation, Dept. of Theoretical Physics, University of Edinburgh, Edinburgh, U.K.
Simpson, A. R., Dandy, G. C. and Murphy, L. J.: 1994, Genetic algorithms compared with other techniques for pipe optimization, J. Water Resour. Plann. Manage. 120(4).
Wang, Q. J.: 1991, The genetic algorithm and its application to calibrating conceptual rainfall-runoff models, Water Resour. Res. 27(9), 2467.
Wright, A.: 1991, Genetic algorithms for real parameter optimization, in: G. J. E. Rawlins, (ed.), Foundations of Genetic Algorithms, Morgan Kaufmann, San Mateo, CA, pp. 205-218.
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Chang, FJ., Chen, L. Real-Coded Genetic Algorithm for Rule-Based Flood Control Reservoir Management. Water Resources Management 12, 185–198 (1998). https://doi.org/10.1023/A:1007900110595
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DOI: https://doi.org/10.1023/A:1007900110595