Abstract
One excellent crossover method for the real-coded genetic algorithm (RGA) is the unimodal normal distribution crossover method (UNDX). The UNDX is superior to the blend crossover method (BLX). The UNDX uses Gaussian distribution functions based on the main and sub searching lines. In this article, we present a method of improving the searching performance of the RGA. We propose the use of biased probability distribution functions (BPDFs) based on the main and sub searching lines in the crossover process. The crossover with BPDFs frequently produces offspring that are close to the best individuals in the current generation, and it is highly likely that these offspring will offer the best solution to the problem. Furthermore, we propose a mutation that has a constant and extended range that is wider than that of the UNDX. Simulations show the efficiency of the proposed method.
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This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006
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Nakanishi, H., Kinjo, H., Oshiro, N. et al. Searching performance of a real-coded genetic algorithm using biased probability distribution functions and mutation. Artif Life Robotics 11, 37–41 (2007). https://doi.org/10.1007/s10015-006-0396-6
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DOI: https://doi.org/10.1007/s10015-006-0396-6