Fitness Landscape Analysis of Hyper-Heuristic Transforms for the Vertex Cover Problem

Hyper-heuristics have recently proved efficient in several areas of combinatorial search and optimization, especially scheduling. The basic idea of hyperheuristics is based on searching for search-strategy. Instead 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 explained 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 original space. In this paper, we analyse properties of fitness landscapes of the problem of minimal vertex cover. We focus on properties 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-heuristicspace really has some more favorable properties than the original space.

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