A Genetic Programming Hyper-Heuristic Approach for Evolving Two Dimensional Strip Packing Heuristics

We present a genetic programming system to evolve reusable heuristics for the two dimensional strip packing problem. The evolved heuristics are constructive, and decide both which piece to pack next and where to place that piece, given the current partial solution. This work contributes to a growing research area which represents a paradigm shift in search methodologies. Instead of using evolutionary computation to search a space of solutions, we employ it to search a space of heuristics for the problem. One of the motivations for this research area is that once a heuristic has been evolved, it can be reused on any new problem instance, meaning that the time consuming evolutionary process need only be run once to obtain a solution to many problem instances. A second motivation is to research methods to automate the heuristic design process. It has been stated in the literature that humans are very good at identifying good building blocks for solution methods, however the task of intelligently searching through all of the potential combinations of these components may be better suited to a computer. With such tools at their disposal, heuristic designers are then free to commit more of their time to the creative process of determining good components, while the computer takes on some of the design process by intelligently combining these components. The contribution of this paper is to show that a genetic programming hyper-heuristic can be employed to automatically generate heuristics which are often better than the human-designed state of the art constructive heuristics, in a very well studied area.