Making use of observable parameters in evolutionary dynamic optimization

In evolutionary dynamic optimization (EDO), most of the existing studies have assumed that dynamic optimization problems are black boxes. However, for many real-world problems, the dynamic parameters that cause the problems to change are observable. However, determining the utility of these parameters in improving optimization performance has not yet been well studied. In this paper, we propose and compare three strategies for this task: rote learning, fitting data with a feedforward neural network and an ensemble strategy. The main idea of these strategies is to learn the relation between the observable parameters and the optimal solutions and then predict new optima once the environment changes. We also propose a set of test cases representing different kinds of characteristics of real-world problems. In the experiments, the proposed strategies are compared with existing methods that do not use observable parameters, and the results validate our proposed strategies.