Clustering of Local Optima in Combinatorial Fitness Landscapes

Gabriela Ochoa (1) , Sébastien Verel (2, 3) , Fabio Daolio (4) , Marco Tomassini (4)

1 School of Computer Science
2 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Groupe SCOBI
3 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
4 ISI - Institut des systèmes d'information

Using the recently proposed model of combinatorial landscapes: local optima networks, we study the distribution of local optima in two classes of instances of the quadratic assignment problem. Our results indicate that the two problem instance classes give rise to very different configuration spaces. For the so-called real-like class, the optima networks possess a clear modular structure, while the networks belonging to the class of random uniform instances are less well partitionable into clusters. We briefly discuss the consequences of the findings for heuristically searching the corresponding problem spaces.

Domaines: Intelligence artificielle [cs.AI]

Gabriela Ochoa, Sébastien Verel, Fabio Daolio, Marco Tomassini. Clustering of Local Optima in Combinatorial Fitness Landscapes. Learning and Intelligent OptimizatioN Conference (LION 5), Jan 2011, Rome, Italy. pp.454--457, ⟨10.1007/978-3-642-25566-3_35⟩. ⟨hal-00550355⟩