Zero knowledge and the chromatic number
Journal of Computer and System Sciences - Eleventh annual conference on structure and complexity 1996
On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Smallest-last ordering and clustering and graph coloring algorithms
Journal of the ACM (JACM)
New methods to color the vertices of a graph
Communications of the ACM
Efficient Graph Coloring by Evolutionary Algorithms
Proceedings of the 6th International Conference on Computational Intelligence, Theory and Applications: Fuzzy Days
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A Novel Bi-objective Genetic Algorithm for the Graph Coloring Problem
ICCMS '10 Proceedings of the 2010 Second International Conference on Computer Modeling and Simulation - Volume 04
Journal of Computer and System Sciences
Multiobjective network topology design
Applied Soft Computing
New order-based crossovers for the graph coloring problem
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Mechanical Design Optimization Using Advanced Optimization Techniques
Mechanical Design Optimization Using Advanced Optimization Techniques
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Bi-objective graph coloring problem (BOGCP) is a generalized version in which the number of colors used to color the vertices of a graph and the corresponding penalty which incurs due to coloring the end-points of an edge with the same color are simultaneously minimized. In this paper, we have analyzed the graph density, the interconnection between high degree nodes of a graph, the rank exponent of the standard benchmark input graph instances and observed that the characterization of graph instances affects on the behavioral quality of the solution sets generated by existing heuristics across the entire range of the obtained Pareto fronts. We have used multi-objective evolutionary algorithm (MOEA) to obtain improved quality solution sets with the problem specific knowledge as well as with the embedded heuristics knowledge. To establish this fact for BOGCP, hybridization approach is used to construct recombination operators and mutation operators and it is observed from empirical results that the embedded problem specific knowledge in evolutionary operators helps to improve the quality of solution sets across the entire Pareto front; the nature of problem specific knowledge differentiates the quality of solution sets.