Adaptive evolutionary algorithm based on population dynamics for dynamic environments

Abstract

In dynamic environments, the absence of diversity may degrade the performance of evolutionary algorithms (EAs). In a previous article, we introduced an method, diversity-reference adaptive control (DRAC), to control population diversity based on reference diversity. DRAC aims to track an appropriate diversity level through a control-based strategy. In such a strategy, the evolutionary process is seen as a control problem, in which the process output is the population diversity and the process input is one or more EA adjustable parameters. In that first version of DRAC, the evolutionary process is treated as a black box, thus, the updating of the control variables is made as a function of the error between the population diversity and the reference-model diversity. The DRAC approach does not consider sensitivity analysis. In the current version, a population dynamics model is used to describe the evolutionary process and to allow the control variables updating.