Robust and Efficient Genetic Algorithms with Hierarchical Niching and a Sustainable Evolutionary Computation Model

This paper proposes a new niching method named hierarchical niching, which combines spatial niching in search space and a continuous temporal niching concept. The method is naturally implemented as a new genetic algorithm, QHFC, under a sustainable evolutionary computation model: the Hierarchical Fair Competition (HFC) Model. By combining the benefits of the temporally continuing search capability of HFC and this spatial niching capability, QHFC is able to achieve much better performance than deterministic crowding and restricted tournament selection in terms of robustness, efficiency, and scalability, simultaneously, as demonstrated using three massively multi-modal benchmark problems. HFC-based genetic algorithms with hierarchical niching seem to be very promising for solving difficult realworld problems.