An Approximation Algorithm for Diagnostic Test Scheduling in Multicomputer Systems
IEEE Transactions on Computers
On the hardness of approximating minimization problems
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Register allocation in structured programs
Journal of Algorithms - Special issue on SODA '95 papers
New methods to color the vertices of a graph
Communications of the ACM
Scheduling in Computer and Manufacturing Systems
Scheduling in Computer and Manufacturing Systems
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Graph Coloring with Adaptive Evolutionary Algorithms
Journal of Heuristics
Constraint Propagation in Graph Coloring
Journal of Heuristics
Finding the chromatic number by means of critical graphs
Journal of Experimental Algorithmics (JEA)
Graph theory: An algorithmic approach (Computer science and applied mathematics)
Graph theory: An algorithmic approach (Computer science and applied mathematics)
Combinatorial optimization in system configuration design
Automation and Remote Control
A cellular learning automata-based algorithm for solving the vertex coloring problem
Expert Systems with Applications: An International Journal
Note: Three new upper bounds on the chromatic number
Discrete Applied Mathematics
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Graph coloring is one of the hardest combinatorial optimization problems for which a wide variety of algorithms has been proposed over the last 30 years. The problem is as follows: given a graph one has to assign a label to each vertex such that no monochromatic edge appears and the number of different labels used is minimized. In this paper we present a new heuristic for this problem which works with two different functionalities. One is defined by two greedy subroutines, the former being a greedy constructive one and the other a greedy modification one. The other functionality is a perturbation subroutine, which can produce also infeasible colorings, and the ability is then to retrieve feasible solutions. In our experimentation the proper tuning of this optimization scheme produced good results on known graph coloring benchmarks.