Genetic Operators Design Using Division Algorithm in the Integer Solution Space
Document Type
Conference Proceeding
Publication Date
11-27-2006
Abstract
Genetic algorithm (GA) is a well known algorithm applied to a wide variety of optimization problems [4]. It combines selection, crossover, and mutation operators in order to find the best solution to a problem. The standard GA operates on chromosomes represented by binary code strings [1, 2]. This paper designs alternative operators in the GA process. The new operations reduce the binary decoding process of chromosomes when performing the computation. Variations of solutions with the implemented operations on chromosomes are studied. Computational examples show that the new methods save the computer time and enhance the efficiency when compared to the standard GA.
MSU Digital Commons Citation
Guiting, Li; Bingtuan, Wang; and Li, Aihua, "Genetic Operators Design Using Division Algorithm in the Integer Solution Space" (2006). Department of Mathematics Facuty Scholarship and Creative Works. 82.
https://digitalcommons.montclair.edu/mathsci-facpubs/82