RT Conference Proceedings
T1 Local Optima Networks, Landscape Autocorrelation and Heuristic Search Performance
A1 Chicano-García, José-Francisco
A1 Daolio, Fabio
A1 Ochoa, Gabriela
A1 Verel, Sébastien
A1 Tomassini, Marco
A1 Alba-Torres, Enrique
K1 Algoritmos computacionales
AB 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.
YR 2014
FD 2014-10-06
LK http://hdl.handle.net/10630/8194
UL http://hdl.handle.net/10630/8194
LA eng
NO Chicano, F., Daolio F., Ochoa G., Vérel S., Tomassini M., & Alba E. (2012).  Local Optima Networks, Landscape Autocorrelation and Heuristic Search Performance. (Coello, C. A. Coello, Cutello V., Deb K., Forrest S., Nicosia G., & Pavone M., Ed.).Parallel Problem Solving from Nature - PPSN XII - 12th International Conference, Taormina, Italy, September 1-5, 2012, Proceedings, Part II. 337–347.
NO Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.                Spanish Ministry of Science and Innovation and FEDER under contract TIN2011-28194. Andalusian Government under contract P07-TIC-03044. Swiss National Science Foundation for financial support under grant number 200021-124578.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026