A genetic programming based hyper-heuristic approach for combinatorial optimisation

Authors:
Su Nguyen;Mengjie Zhang;Mark Johnston
Affiliations:
Victoria University of Wellington, Wellington, New Zealand;School of Engineering and Computer Science, Wellington, New Zealand;Victoria University of Wellington, Wellington, New Zealand
Venue:
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Year:
2011

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Abstract

Genetic programming based hyper-heuristics (GPHH) have become popular over the last few years. Most of these proposed GPHH methods have focused on heuristic generation. This study investigates a new application of genetic programming (GP) in the field of hyper-heuristics and proposes a method called GPAM, which employs GP to evolve adaptive mechanisms (AM) to solve hard optimisation problems. The advantage of this method over other heuristic selection methods is the ability of evolved adaptive mechanisms to contain complicated combinations of heuristics and utilise problem solving states for heuristic selection. The method is tested on three problem domains and the results show that GPAM is very competitive when compared with existing hyper-heuristics. An analysis is also provided to gain more understanding of the proposed method.