Abstract
Hyper-heuristics have recently proved efficient in several areas of combinatorial search and optimiza- tion, especially scheduling. The basic idea of hyper- heuristics is based on searching for search-strategy.
In- stead of traversing the solution-space, the hyper-heuristic traverses the space of algorithms to find or construct an algorithm best suited for the given problem instance. The observed efficiency of hyper-heuristics is not yet fully ex- plained on the theoretical level.
The leading hypothesis suggests that the fitness landscape of the algorithm-space is more favorable to local search techniques than the orig- inal space. In this paper, we analyse properties of fitness landscapes of the problem of minimal vertex cover.
We focus on prop- erties that are related to efficiency of metaheuristics such as locality and fitness-distance correlation. We compare properties of the original space and the algorithm space trying to verify the hypothesis explaining hyper-heuristics performance.
Our analysis shows that the hyper-heuristic- space really has some more favorable properties than the original space.