Local Optima Networks, Landscape Autocorrelation and Heuristic Search Performance
Francisco Chicano, Fabio Daolio (ISI), Gabriela Ochoa, S\'ebastien, Verel (INRIA Lille - Nord Europe), Marco Tomassini (ISI), Enrique Alba
TL;DR
This paper combines Local Optima Networks and Elementary Landscapes theory to analyze and predict heuristic search performance on Quadratic Assignment Problem instances.
Contribution
It introduces a novel approach integrating LON and landscape autocorrelation to forecast search algorithm effectiveness.
Findings
Network measures correlate with search performance
Landscape autocorrelation predicts heuristic success
Results are based on extensive statistical analysis
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.
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