Learning a procedure that can solve hard bin-packing problems: A new GA-based approach to hyper-heuristics

Abstract

The idea underlying hyper-heuristics is to discover some combination of familiar, straightforward heuristics that performs very well across a whole range of problems. To be worthwhile, such a combination should outperform all of the constituent heuristics. In this paper we describe a novel messy-GA-based approach that learns such a heuristic combination for solving one-dimensional bin-packing problems. When applied to a large set of benchmark problems, the learned procedure finds an optimal solution for nearly 80% of them, and for the rest produces an answer very close to optimal. When compared with its own constituent heuristics, it ranks first in 98% of the problems. © Springer-Verlag Berlin Heidelberg 2003.