Graph Partitioning for Independent Sets
This addresses a fundamental problem in computer science with potential applications in optimization and network analysis, but appears incremental as it builds on existing methods.
The paper tackles the problem of computing maximum independent sets in graphs by developing an evolutionary algorithm that uses graph partitioning and local search, and reports outperforming state-of-the-art algorithms on various instances.
Computing maximum independent sets in graphs is an important problem in computer science. In this paper, we develop an evolutionary algorithm to tackle the problem. The core innovations of the algorithm are very natural combine operations based on graph partitioning and local search algorithms. More precisely, we employ a state-of-the-art graph partitioner to derive operations that enable us to quickly exchange whole blocks of given independent sets. To enhance newly computed offsprings we combine our operators with a local search algorithm. Our experimental evaluation indicates that we are able to outperform state-of-the-art algorithms on a variety of instances.