A Study of Transfer Learning in a Generation Constructive Hyper-Heuristic for One Dimensional Bin Packing

Abstract

The research presented in this paper investigates the use of transfer learning in a genetic programming generation constructive hyper-heuristic for discrete optimisation, namely, the one dimensional bin packing problem (1BPP). The source hyper-heuristic solves easy and medium problem instances from the Scholl benchmark set and the target hyper-heuristic solves the hard problem instances in the same benchmark set. Performance is assessed in terms of objective value, i.e. the number of bins, computational effort and generality of the hyper-heuristic. This study firstly compares the performance of two transfer learning approaches previously shown to be effective for generation constructive hyper-heuristics, for the one dimensional bin packing problem. Both these approaches performed better than not using transfer learning, with the approach transferring the best elements from each generation of the source hyper-heuristic to the target hyper-heuristic (TL2) producing the best results. The study then investigated transferring knowledge on an area of the search space rather than a point in the search space. Three approaches were developed and evaluated for this purpose. Two of these approaches were able to improve the performance of TL2 on three of the ten problem instances with respect to objective value.