Prediction in evolutionary algorithms for dynamic environments

Abstract

Evolutionary algorithms have been widely used to solve dynamic optimization problems. Memory-based evolutionary algorithms are often used when the dynamics of the environment follow some repeated behavior. Over the last few years, the use of prediction mechanisms combined with memory has been explored. These prediction techniques are used to avoid the decrease of the algorithm's performance when a change occurs. This paper investigates the use of prediction methods in memory-based evolutionary algorithms for two distinct situations: to predict when the next change will happen and how the environment will change. For the first predictor two techniques are explored, one based on linear regression and another supported by nonlinear regression. For the second, a technique based on Markov chains is explored. Several experiments were carried out using different types of dynamics in two benchmark problems. Experimental results show that the incorporation of the proposed prediction techniques efficiently improves the performance of evolutionary algorithms in dynamic optimization problems.