Multitask Learning in Hyper-Heuristic Domain with Dynamic Production Scheduling

Abstract

This chapter attempts to solve multiple related dynamic scheduling tasks simultaneously, and adapts traditional multitask learning into the hyper-heuristic domain with genetic programming for this purpose. This chapter verifies the effectiveness of traditional multitask learning in genetic programming for dynamic scheduling and identifies a number of differences that need to be adapted. This chapter develops a multi-population multitask learning framework with genetic programming. In addition, this chapter proposes an effective origin-based offspring reservation strategy to keep the quality of individuals for one task and learn knowledge from other tasks. The learned scheduling heuristics show that the subpopulations do manage to learn from each other to solve tasks.