A study of permutation crossover operators on the traveling salesman problem
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
New methods to color the vertices of a graph
Communications of the ACM
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Graph Coloring with Adaptive Evolutionary Algorithms
Journal of Heuristics
A Grouping Genetic Algorithm for Graph Colouring and Exam Timetabling
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Multi-objective Genetic Algorithms for grouping problems
Applied Intelligence
A wide-ranging computational comparison of high-performance graph colouring algorithms
Computers and Operations Research
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Huge color class redundancy makes the graph coloring problem (GCP) very challenging for genetic algorithms (GAs), and designing effective crossover operators is notoriously difficult. Thus, despite the predominance of population based methods, crossover plays a minor role in many state-of-the-art approaches to solving the GCP. Two main encoding methods have been adopted for heuristic and GA methods: direct encoding, and order based encoding. Although more success has been achieved with direct approaches, algorithms using an order based representation have one powerful advantage: every chromosome decodes as a feasible solution. This paper introduces some new order based crossover variations and demonstrates that they are much more effective on the GCP than other order based crossovers taken from the literature.