An Improved Meta-Genetic Algorithm for Hybridizing Metaheuristics
Ahmed Nazar Hassan, Nelishia Pillay · 2018
Previous work has established the effectiveness of meta-genetic algorithms for the automated design of hybrid metaheuristics. The design decisions made by the meta-genetic algorithm include which metaheuristics to combine, the order of the metaheuristics in the combination and the parameter values to use for each metaheuristic. This research is still in its infancy and the research presented in this paper aims to improve on this initial study. Two areas for improvement in applying meta-genetic algorithms for hybridizing metaheuristics in this way have been identified. The first is to reduce the parameter space explored by the meta-genetic algorithm. Orthogonal arrays are examined for this purpose. The second improvement is in terms of the search by improving exploitation of the meta-genetic algorithm. An extension of the fitness-based scanning crossover is investigated as an alternative to one-point crossover to achieve this. The aircraft landing problem (ALP) is used to test these improvements. The evolved hybrid metaheuristics are found to perform competitively with the state-of-the-art methods and outperform the automatically tuned metaheuristics when used individually. The evolved hybrid metaheuristics demonstrate the reusability of the meta-genetic algorithm as they are designed using a relatively small training set and generalize to the whole dataset. In future work, other mechanisms for hybridizing metaheuristics shall be considered.