Recent developments in fitness landscape analysis include the study of Local Optima Networks (LON) and applications of the Elementary Landscapes theory. This paper represents a first step at combining these two tools to explore their ability to forecast the performance of search algorithms. We base our analysis on the Quadratic Assignment Problem (QAP) and conduct a large statistical study over 600 generated instances of different types. Our results reveal interesting links between the network measures, the autocorrelation measures and the performance of heuristic search algorithms.
@article{arxiv.1210.4021,
title = {Local Optima Networks, Landscape Autocorrelation and Heuristic Search Performance},
author = {Francisco Chicano and Fabio Daolio and Gabriela Ochoa and Sébastien Verel and Marco Tomassini and Enrique Alba},
journal= {arXiv preprint arXiv:1210.4021},
year = {2012}
}
Comments
Parallel Problem Solving from Nature - PPSN XII, Taormina : Italy (2012)