On the performance of a hybrid genetic algorithm in dynamic environments

TL;DR

This paper evaluates a hybrid genetic algorithm's effectiveness in tracking optimal solutions in dynamic environments, demonstrating its superior performance over other evolutionary algorithms across various conditions.

Contribution

It introduces and tests a hybrid genetic algorithm specifically designed for dynamic environments, showing improved tracking capabilities compared to existing methods.

Findings

HGA outperforms other algorithms in tracking dynamic optima.

Performance varies with environment dimensions, update frequency, and displacement strength.

HGA maintains better solution quality over time in dynamic settings.

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.