Diversity-based model reference for genetic algorithms in dynamic environment

Preservation of diversity in the evolutionary process is crucial to solve problems considering dynamic environments. This work proposes an adaptive evolutionary algorithm to control the population diversity based on a diversity function. The evolutionary process searches for the optimum while the diversity is controlled to track the diversity function. To control the population diversity, the proposed method creates a selection mechanism to adjust the fitnesses of a part of the population based on a fitness penalty. The proposed adaptive method uses the model-reference adaptive system as the control strategy to adjust the fitness penalty parameter. The proposed method is called diversity-reference adaptive control (DRAC). The performance of DRAC method was evaluated for multimodal and dynamic test functions. The results show that DRAC method often reached the optimum area, following environment changes, faster than SGA.