Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
New methods to color the vertices of a graph
Communications of the ACM
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
An Experimental Investigation of Iterated Local Search for Coloring Graphs
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
A graph coloring heuristic using partial solutions and a reactive tabu scheme
Computers and Operations Research
An improved ant colony optimisation heuristic for graph colouring
Discrete Applied Mathematics
On the recursive largest first algorithm for graph colouring
International Journal of Computer Mathematics
A Vector Assignment Approach for the Graph Coloring Problem
Learning and Intelligent Optimization
A combinatorial algorithm for the TDMA message scheduling problem
Computational Optimization and Applications
A grasp-knapsack hybrid for a nurse-scheduling problem
Journal of Heuristics
Heuristics for the bandwidth colouring problem
International Journal of Metaheuristics
Coloring large graphs based on independent set extraction
Computers and Operations Research
A wide-ranging computational comparison of high-performance graph colouring algorithms
Computers and Operations Research
On the efficiency of an order-based representation in the clique covering problem
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Metaheuristics for robust graph coloring
Journal of Heuristics
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We first present a literature review of heuristics and metaheuristics developed for the problem of coloring graphs. We then present a Greedy Randomized Adaptive Search Procedure (GRASP) for coloring sparse graphs. The procedure is tested on graphs of known chromatic number, as well as random graphs with edge probability 0.1 having from 50 to 500 vertices. Empirical results indicate that the proposed GRASP implementation compares favorably to classical heuristics and implementations of simulated annealing and tabu search. GRASP is also found to be competitive with a genetic algorithm that is considered one of the best currently available for graph coloring.