Abstract
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. © 2012 Springer-Verlag.
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CITATION STYLE
Chicano, F., Daolio, F., Ochoa, G., Vérel, S., Tomassini, M., & Alba, E. (2012). Local optima networks, landscape autocorrelation and heuristic search performance. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7492 LNCS, pp. 337–347). https://doi.org/10.1007/978-3-642-32964-7_34
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