Automatically Designing More General Mutation Operators of Evolutionary Programming for Groups of Function Classes Using a Hyper-Heuristic

Citation

Hong L, Drake J, Woodward J & Ozcan E (2016) Automatically Designing More General Mutation Operators of Evolutionary Programming for Groups of Function Classes Using a Hyper-Heuristic. In: GECCO '16 Proceedings of the Genetic and Evolutionary Computation Conference 2016. GECCO '16: Genetic and Evolutionary Computation Conference 2016, Denver, CO, USA, 20.07.2016-24.07.2016. New York: ACM, pp. 725-732. https://doi.org/10.1145/2908812.2908958

Abstract
In this study we use Genetic Programming (GP) as an offline hyper-heuristic to evolve a mutation operator for Evolutionary Programming. This is done using the Gaussian and uniform distributions as the terminal set, and arithmetic operators as the function set. The mutation operators are automatically designed for a specific function class. The contribution of this paper is to show that a GP can not only automatically design a mutation operator for Evolutionary Programming (EP) on functions generated from a specific function class, but also can design more general mutation operators on functions generated from groups of function classes. In addition, the automatically designed mutation operators also show good performance on new functions generated from a specific function class or a group of function classes.