An equivalent genetic algorithm based on extended strings and its convergence analysis

Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0020-0255(01)00122-0

Title:	An equivalent genetic algorithm based on extended strings and its convergence analysis
Authors:	Liang, Y. Zhou, C. Wang, Z. Pueh Lee, H. Piang Lim, S.
Keywords:	Convergence analysis Extended-string-based genetic algorithm (ESGA) Global optimal solution Rate of convergence
Issue Date:	Oct-2001
Citation:	Liang, Y., Zhou, C., Wang, Z., Pueh Lee, H., Piang Lim, S. (2001-10). An equivalent genetic algorithm based on extended strings and its convergence analysis. Information Sciences 138 (1-4) : 119-135. ScholarBank@NUS Repository. https://doi.org/10.1016/S0020-0255(01)00122-0
Abstract:	A novel equivalent genetic algorithm based on extended strings (ESGA) is presented in this paper. The ESGA is equivalent to the canonical genetic algorithms (CGAs) in optimization problems, whereas the manner of the group by group implementation makes it different from the conventional genetic algorithms. In this paper the conditions and the rate of convergence of the ESGA are analyzed theoretically. It is shown that the ESGA can converge to the global optimal solution and the average value of the times of reaching the optimal point is finite. An analytical expression for the average rate of convergence of the ESGA is also provided. © 2001 Elsevier Science Inc. All rights reserved.
Source Title:	Information Sciences
URI:	http://scholarbank.nus.edu.sg/handle/10635/59452
ISSN:	00200255
DOI:	10.1016/S0020-0255(01)00122-0
Appears in Collections:	Staff Publications

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