A graph coloring algorithm for large scale scheduling problems
Computers and Operations Research
Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern recognition and image analysis
Pattern recognition and image analysis
New methods to color the vertices of a graph
Communications of the ACM
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Algorithms
Graph Coloring with Adaptive Evolutionary Algorithms
Journal of Heuristics
New Genetic Local Search Operators for the Traveling Salesman Problem
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Diversity Control and Multi-Parent Recombination for Evolutionary Graph Coloring Algorithms
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
Research frontier: memetic computation-past, present & future
IEEE Computational Intelligence Magazine
A Multi-Facet Survey on Memetic Computation
IEEE Transactions on Evolutionary Computation
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The graph coloring problem is one of famous combinatorial optimization problems. Some researchers attempted to solve combinatorial optimization problem with evolutionary algorithm, which can find near optimal solution based on the evolution mechanism of the nature. However, it sometimes requires too much cost to evaluate fitness of a large number of individuals in the population when applying the GA to the real world problems. This paper attempts to solve graph coloring problem using a fuzzy clustering based evolutionary approach to reduce the cost of the evaluation. In order to show the feasibility of the method, some experiments with other alternative methods are conducted.