On the performance of a hybrid genetic algorithm in dynamic environments
This addresses optimization in changing environments for applications like scheduling, but it is incremental as it builds on existing methods.
The paper investigated a hybrid genetic algorithm's ability to track optima in dynamic environments, finding it outperformed some existing evolutionary algorithms in various conditions.
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.