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.Comment: This paper has been submitted to Applied Mathematics and Computation
on May 22, 2012 Revised version has been submitted to Applied Mathematics and
Computation on March 1, 201
