Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, Workshop, October 11-13, 1993
A New Genetic Local Search Algorithm for Graph Coloring
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Genetic algorithms with multi-parent recombination
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
A graph coloring heuristic using partial solutions and a reactive tabu scheme
Computers and Operations Research
An adaptive memory algorithm for the k-coloring problem
Discrete Applied Mathematics
Variable space search for graph coloring
Discrete Applied Mathematics
A Metaheuristic Approach for the Vertex Coloring Problem
INFORMS Journal on Computing
MA|PM: memetic algorithms with population management
Computers and Operations Research
Population-based and learning-based metaheuristic algorithms for the graph coloring problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Two iterative metaheuristic approaches to dynamic memory allocation for embedded systems
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
A mathematical model and a metaheuristic approach for a memory allocation problem
Journal of Heuristics
Solving graph coloring problem by fuzzy clustering-based genetic algorithm
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
| Hi-index | 0.00 |
We present a hybrid evolutionary algorithm for the graph coloring problem (Evocol). Evocol is based on two simple-but-effective ideas. First, we use an enhanced crossover that collects the best color classes out of more than two parents; the best color classes are selected using a ranking based on both class fitness and class size. We also introduce a simple method of using distances to assure the population diversity: at each operation that inserts an individual into the population or that eliminates an individual from the population, Evocol tries to maintain the distances between the remaining individuals as large as possible. The results of Evocol match the best-known results from the literature on almost all difficult DIMACS instances (a new solution is also reported for a very large graph). Evocol obtains these performances with a success rate of at least 50%.