Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
Akaike information criterion statistics
Akaike information criterion statistics
SIAM Review
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
A study of NK landscapes' basins and local optima networks
Proceedings of the 10th annual conference on Genetic and evolutionary computation
NK landscapes, problem difficulty, and hybrid evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
First-improvement vs. best-improvement local optima networks of NK landscapes
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Large-step markov chains for the TSP incorporating local search heuristics
Operations Research Letters
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Local Optima Networks (LONs) have been recently proposed as an alternative model of combinatorial fitness landscapes. The model compresses the information given by the whole search space into a smaller mathematical object that is the graph having as vertices the local optima and as edges the possible weighted transitions between them. A new set of metrics can be derived from this model that capture the distribution and connectivity of the local optima in the underlying configuration space. This paper departs from the descriptive analysis of local optima networks, and actively studies the correlation between network features and the performance of a local search heuristic. The NK family of landscapes and the Iterated Local Search metaheuristic are considered. With a statistically-sound approach based on multiple linear regression, it is shown that some LONs' features strongly influence and can even partly predict the performance of a heuristic search algorithm. This study validates the expressive power of LONs as a model of combinatorial fitness landscapes.