On the performance of a hybrid genetic algorithm in dynamic environments

Authors

Quan Yuan, Wayne State UniversityFollow
Zhixin Yang, Wayne State UniversityFollow

Document Type

Article

Abstract

The ability to track the optimum of dynamic environments is important in many practical applications. In this paper, the capability of a hybrid genetic algorithm (HGA) to track the optimum in some dynamic environments is investigated for different functional dimensions, update frequencies, and displacement strengths in different types of dynamic environments. Experimental results are reported by using the HGA and some other existing evolutionary algorithms in the literature. The results show that the HGA has better capability to track the dynamic optimum than some other existing algorithms.

Disciplines

Mathematics | Other Applied Mathematics

Comments

This is the authors' preprint of an article subsequently published in Applied Mathematics and Computation 219(24) (August 2013).

Recommended Citation

Yuan, Quan; Yang, Zhixin. (2013). On the performance of a hybrid genetic algorithm in dynamic environments. Applied Mathematics and Computation, 219(24): 11408-11413.