Multi-step environment learning classifier systems applied to hyper-heuristics

Heuristic Algorithms (HA) are very widely used to tackle practical problems in operations research. They are simple, easy to understand and inspire confidence. Many of these HAs are good for some problem instances while very poor for other cases. While Meta-Heuristics try to find which is the best heuristic and/or parameters to apply for a given problem instance Hyper-Heuristics (HH) try to combine several heuristics in the same solution searching process, switching among them whenever the circumstances vary. Besides, instead to solve a single problem instance it tries to find a general algorithm to apply to whole families of problems. HH use evolutionary methods to search for such a problemsolving algorithm and, once produced, to apply it to any new problem instance desired. Learning Classifier Systems (LCS), and in particular XCS, represents an elegant and simple way to try to fabricate such a composite algorithm. This represents a different kind of problem to those already studied ...

• Type: Conference
• Year: 2006
• Where: GECCO
• Authors: Javier G. Marín-Blázquez, Sonia Schulenburg
• Keywords: GECCO 2006, Optimization, Problem Instance, Problem Instance Hyper-heuristics, Single Step Environments