Evolutionary algorithms (EAs) can be used to find solutions in dynamic environments. In such cases, after a change in the environment, EAs can either be restarted or they can take advantage of previous knowledge to resume the evolutionary process. The second option tends to be faster and demands less computational effort. The preservation or growth of population diversity is one of the strategies used to advance the evolutionary process after modifications to the environment. We propose a new adaptive method to control population diversity based on a model-reference. The EA evolves the population whereas a control strategy, independently, handles the population diversity. Thus, the adaptive EA evolves a population that follows a diversity-reference model. The proposed model, called the Diversity-Reference Adaptive Control Evolutionary Algorithm (DRAC), aims to maintain or increase the population diversity, thus avoiding premature convergence, and assuring exploration of the solution space during the whole evolutionary process. We also propose a diversity models based on the dynamics of heterozygosity of the population, as models to be tracked by the diversity control. The performance of DRAC showed promising results when compared with the standard genetic algorithm and six other adaptive evolutionary algorithms in 14 different experiments with three different types of environments.
Explicit Memory Schemes for Evolutionary Algorithms in Dynamic Environments
