Providing a memory mechanism to enhance the evolutionary design of heuristics

Genetic programming approaches have previously been employed in the literature to evolve heuristics for various combinatorial optimisation problems. This paper presents a hyper-heuristic genetic programming methodology to evolve more sophisticated one dimensional bin packing heuristics than have been evolved previously. The heuristics have access to a memory, which allows them to make decisions with some knowledge of their potential future impact. In contrast to previously evolved heuristics for this problem, we show that these heuristics evolve to draw upon this memory in order to facilitate better planning, and improved packings. This fundamental difference enables an evolved heuristic to represent a dynamic packing strategy rather than a fixed packing strategy. A heuristic can change its behaviour depending on the characteristics of the pieces it has seen before, because it has evolved to draw upon its experience.