Improving the performance guarantee for approximate graph coloring
Journal of the ACM (JACM)
New approximation algorithms for graph coloring
Journal of the ACM (JACM)
Zero knowledge and the chromatic number
Journal of Computer and System Sciences - Eleventh annual conference on structure and complexity 1996
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
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Graph Coloring with Adaptive Evolutionary Algorithms
Journal of Heuristics
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
A Novel Parallel Genetic Algorithm for the Graph Coloring Problem in VLSI Channel Routing
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
Approximate graph coloring by semidefinite programming
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
New order-based crossovers for the graph coloring problem
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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We consider a formulation of the biobjective soft graph coloring problem so as to simultaneously minimize the number of colors used as well as the number of edges that connect vertices of the same color. We solve this problem using well-known multiobjective evolutionary algorithms (MOEA), and observe that they show good diversity and (local) convergence. Then, we consider and adapt the single objective heuristics to yield a Pareto-front and observe that the quality of solutions obtained by MOEAs is much inferior. We incorporate the problem specific knowledge into representation and reproduction operators, in an incremental way, and get good quality solutions using MOEAs too. The spin-off point we stress with this work is that, for real world applications of unknown nature, it is indeed difficult to realize how good/bad the quality of the solutions obtained is.