Improving Performance in Combinatorial Optimisation Using Averaging and Clustering

In a recent paper an algorithm for solving MAX-SAT was proposed which worked by clustering good solutions and restarting the search from the closest feasible solutions. This was shown to be an extremely effective search strategy, substantially out-performing traditional optimisation techniques. In this paper we extend those ideas to a second classic NP-Hard problem, namely Vertex Cover. Again the algorithm appears to provide an advantage over more established search algorithms, although it shows different characteristics to MAX-SAT. We argue this is due to the different large-scale landscape structure of the two problems.

• Type: Conference
• Year: 2009
• Where: EVOW
• Authors: Mohamed Qasem, Adam Prügel-Bennett
• Keywords: Artificial Intelligence, Classic Np-hard Problem, Closest Feasible Solutions, EVOW 2009, Effective Search Strategy