Nonlinear Integer, Discrete and Continuous Optimization Using Adaptive Range Genetic Algorithms

Abstract

Abstract Genetic algorithms are effective algorithms for large scaled combinatorial optimization. They are potentially effective in integer and discrete optimization. However, as they are not well coded to its tedious expression in converting chromosomes to design variables, we need to do some special efforts to overcome these flaws. In the proposed method, it automatically adapts searching ranges according to the situation of the generation. Thus, we are free from these flaws. Moreover, we don’t have to give too many genes to chromosome, we can save computational time and memory and the convergence becomes better. In this paper, we combine the proposed integer and discrete adaptive range genetic algorithms and adaptive real range genetic algorithms which we presented in the previous studies, and present an extended genetic algorithms method. We applied the proposed method to well-known test problems, compare the results with the other methods and show its effectiveness.