G. Li, B. Wang (PRC), and A. Li (USA)
genetic operator/algorithm, selection, crossover, mutation
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. Varia tions of solutionswith the implemented operations on chro mosomes are studied. Computational examples show that the new methods save the computer time and enhance the efficiency when compared to the standard GA.
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