New methods to color the vertices of a graph
Communications of the ACM
A GRASP for Coloring Sparse Graphs
Computational Optimization and Applications
Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, Workshop, October 11-13, 1993
Efficient Graph Coloring by Evolutionary Algorithms
Proceedings of the 6th International Conference on Computational Intelligence, Theory and Applications: Fuzzy Days
A survey of local search methods for graph coloring
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
Less is More: Sparse Graph Mining with Compact Matrix Decomposition
Statistical Analysis and Data Mining
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Computers and Operations Research
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Bioinspired Computation in Combinatorial Optimization: Algorithms and Their Computational Complexity
Computer Science Review
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Although the (vertex) clique covering problem (CCP) is a classical NP-hard problem, it is still overlooked in the fields of heuristics and evolutionary algorithms. We present two main results concerning this problem. First, we propose a genotype-phenotype mapping algorithm for an order-based representation of the CCP, called greedy clique covering (GCC), and prove that for an arbitrary graph, there is a permutation, for which GCC constructs the optimal solution. Although the greedy graph coloring can also be used as genotype-phenotype mapping, we show that GCC is much more efficient for sparse graphs. Secondly, we adapt a mutation-based metaheuristic algorithm using the order-based representation - iterated greedy (IG), to solve the CCP. On sparse graphs with planted cliques, we provide empirical evidence that IG outperforms an exact algorithm. This result is supported by a runtime analysis of IG on several subclasses of graphs with planted cliques. We include experimental results of IG on random graphs, several DIMACS instances and social graphs. Its comparison to the related existing approaches shows that our IG algorithm outperforms the standard approaches in almost all instances.