Abstract
Genetic algorithms are known to be efficient for global optimizing. However, they are not well suited to perform finely-tuned local searches and are prone to converge prematurely before the best solution has been found. This paper uses genetic diversity measurements to prevent premature convergence and a hybridizing genetic algorithm with simplex downhill method to speed up convergence. Three case studies show the procedure to be efficient, tough, and robust.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Holland, J., Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, Michigan, 1975.
Reeves, C., and Wright, C., Genetic Algorithms and Statistical Methods: A Comparison, First IEE/IEEE International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, IEE Conference Publication No. 414, pp. 137-140, 1995.
Grefenstette, J., Incorporating Problem-Specific Knowledge into Genetic Algorithms, Genetic Algorithms and Simulated Annealing, Edited by L. Davis, Morgan Kaufmann Publishers, Los Altos, California, pp. 42-60, 1987.
Goldberg, D., Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley Publishing Company, Reading, Massachusetts, 1989.
Ritzel, B., and Eheart, W., Using Genetic Algorithms to Solve a Multiple-Objective Groundwater Pollution Contaminant Problem, Water Resources Research, Vol. 30, pp. 1589-1603, 1994.
Davis, L., and Steenstrup, M., Genetic Algorithms and Simulated Annealing: An Overview, Genetic Algorithms and Simulated Annealing, Edited by L. Davis, Morgan Kaufmann Publishers, Low Altos, California, pp. 1-11, 1987.
Nelder, J. A., and Mead, R., Method for Function Minimization, Computer Journal, Vol. 7, pp. 308-313, 1965.
BÄck, T., Evolution Strategies: An Alternative Evolutionary Algorithm, Artificial Evolution: European Conference Selected Papers, Edited by J. Alliot, E. Lutton, E. Ronald, M. Schoenauer, and D. Snyers, Lecture Notes in Computer Science, Springer, Berlin, Germany, Vol. 1063, pp. 1-20, 1995.
Yang, R., Application of Neural Networks and Genetic Algorithms to Modelling Flood Discharges and Urban Water Quality, PhD Thesis, University of Manchester, 1997.
Booker, L., Improving Search in Genetic Algorithms, Genetic Algorithms and Simulated Annealing, Edited by L. Davis, Morgan Kaufmann Publishers, Los Altos, California, pp. 61-73, 1987.
Wang, Q., The Genetic Algorithm and Its Application to Calibration Conceptual Rainfall-Runoff Models, Water Resources Research, Vol. 27, pp. 2467-2471, 1991.
Franchini, M., Use of a Genetic Algorithm Combined with a Local Search Method for the Automatic Calibration of Conceptual Rainfall-Runoff Models, Hydrological Sciences Journal, Vol. 41, pp. 21-39, 1996.
Hooke, R., and Jeeves, T., Direct Search Solution of Numerical and Statistical Problems, Journal of the Association for Computing Machinery, Vol. 8, pp. 212-221, 1961.
Spears, W., Crossover or Mutation? Foundations of Genetic Algorithms, Edited by L. Whitley, Morgan Kaufmann Publishers, Los Altos, California, Vol. 2, pp. 221-237, 1993.
Syswerda, G., Simulated Crossover in Genetic Algorithms, Foundations of Genetic Algorithms, Edited by L. Whitley, Morgan Kaufmann Publishers, Los Altos, California, Vol. 2, pp. 239-255, 1993.
TÖrn, A., and Zilinskas, A., Global Optimization, Lecture Notes in Computer Science, Springer Verlag, Berlin, Germany, Vol. 350, 1989.
Polovinkin, A., Automation of Searching Design, Radio i Sviaz, Moscow, Russia, 1981.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Yang, R., Douglas, I. Simple Genetic Algorithm with Local Tuning: Efficient Global Optimizing Technique. Journal of Optimization Theory and Applications 98, 449–465 (1998). https://doi.org/10.1023/A:1022697719738
Issue Date:
DOI: https://doi.org/10.1023/A:1022697719738