Niching Genetic Programming based Hyper-heuristic Approach to Dynamic Job Shop Scheduling

Abstract

This paper investigates the application of fitness sharing to a coevolutionary genetic programming based hyper-heuristic (GP-HH) approach to a dynamic job shop scheduling (DJSS) problem that evolves an ensemble of dispatching rules. Evolving ensembles using GP-HH for DJSS problem is a relatively unexplored area, and has been shown to outperform standard GP-HH procedures that evolve single rules. As a fitness sharing algorithm has not been applied to the specific GP-HH approach, we investigate four different phenotypic distance measures as part of a fitness sharing algorithm. The fitness sharing algorithm may potentially improve the diversity of the constituent members of the ensemble and improve the quality of the ensembles. The results show that the niched coevolutionary GP approaches evolve smaller sized rules than the base coevolutionary GP approaches, but have similar performances.