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oai:riuma.uma.es:10630/81942026-02-03T11:47:04Zcom_10630_2254col_10630_37959

Local Optima Networks, Landscape Autocorrelation and Heuristic Search Performance Chicano-García, José-Francisco Daolio, Fabio Ochoa, Gabriela Verel, Sébastien Tomassini, Marco Alba-Torres, Enrique Algoritmos computacionales 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. 2014-10-06T11:01:28Z 2014-10-06T11:01:28Z 2014-10-06T11:01:28Z 2014-10-06 conference output http://hdl.handle.net/10630/8194 eng Parallel Problem Solving from Nature Taormina, Italy 1/9/2012 open access