Local optima networks and the performance of iterated local search
This work provides insights into heuristic algorithm behavior for combinatorial optimization problems, but it is incremental as it builds on existing LON models.
The paper investigates the relationship between Local Optima Networks (LONs) features and the performance of Iterated Local Search on NK landscapes, showing that some LONs features strongly influence and can partly predict heuristic performance.
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.