Research on Extreme Points Optimizing of Nonlinear Multi-Peak Function Based on Genetic Algorithm
In the application of Genetic Algorithm (GA) to solve the function optimization problem, different encoding methods have different effect on performance of GA. Aiming at the global optimization problem of a class of nonlinear multi-peak function, the paper utilized binary coding and floating coding methods for genetic optimization and analyzed their performance. The experimental result of four kinds of typical nonlinear multi-peak function showed that under the precondition of given genetic operator, the optimizing performance of floating coding method to optimize nonlinear multi-peak function with isolated extreme points is less that the binary coding. The tuning ability of floating coding is stronger. As to the ordinary multi-peak function, the search affect is better than binary coding.
Donald C. Wunsch II, Honghua Tan, Dehuai Zeng, Qi Luo
L. G. Yang "Research on Extreme Points Optimizing of Nonlinear Multi-Peak Function Based on Genetic Algorithm", Advanced Materials Research, Vols. 121-122, pp. 304-308, 2010