A Hyper-Heuristic Framework with XCS: Learning to Create Novel Problem-Solving Algorithms Constructed from Simpler Algorithmic Ingredients

Evolutionary Algorithms (EAs) have been successfully reported by academics in a wide variety of commercial areas. However, from a commercial point of view, the story appears somewhat different; the number of success stories does not appear to be as significant as those reported by academics. For instance, Heuristic Algorithms (HA) are still very widely used to tackle practical problems in operations research, where many of these are NP-hard and exhaustive search is often computationally intractable. There are a number of logical reasons why practitioners do not embark so easily in the development and use of EAs. This work is concerned with a new line of research based on bringing together these two approaches in a harmonious way. The idea is that instead of using an EA to learn the solution of a specific problem, use it to find an algorithm, i.e. a solution process that can solve well a large family of problems by making use of familiar heuristics. The work of the authors is novel in two ways: within the Learning Classifier Systems (LCS) current body of research, it represents the first attempt to tackle the Bin Packing problem (BP), a different kind of problem to those already studied by the LCS community, and from the Hyper-Heuristics (HH) framework, it represents the first use of LCS as the learning paradigm. Several reward schema based on single or multiple step environments are studied in this paper, tested on a very large set of BP problems and a small set of widely used HAs. Results of the approach are encouraging, showing outperformance over all HAs used individually and over previously reported work by the authors, including non-LCS (a GA based approach used for the same BP set of problems) and LCS (using single step environments). Several findings and future lines of work are also outlined.