Randomized Population with Taguchi’s Method for Multi-objective Optimization

Abstract

Genetic algorithms can be divided into two categories: single objective and multiple objectives. With single objective, we introduce two modified genetic algorithms: the orthogonal genetic algorithm with quantization (OGA/Q), which utilizes the orthogonal design and quantization technique, and the Hybrid Taguchi Genetic Algorithm (HTGA), which utilizes the Taguchi’s method. Because of the multiple objective functions, the design of the multi-objective genetic algorithms focuses on the fitness assignment, the diversity preservation, and the addition of an elite set. In this paper, we propose to include an additional random population besides the original initial population. In each generation we replace the random population and select only the non-dominated individuals into the elite set. The proposed method can explore more general solution space and can locate better solutions. We then apply Taguchi’s method to generate better individuals in the additional random population.1