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

Jianjun Hu¹ and Erik Goodman²

¹Genetic Algorithm Research and Application Group, (GARAGe), Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48823
Hujianju@egr.msu.edu

²Genetic Algorithm Research and Application Group (GARAGe), Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48823
Goodman@egr.msu.edu

Abstract. 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 real-world problems.

LNCS 3102, p. 1220 ff.

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