Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
Almost all k-colorable graphs are easy to color
Journal of Algorithms
A randomised 3-colouring algorithm
Discrete Mathematics - Graph colouring and variations
The priority-based coloring approach to register allocation
ACM Transactions on Programming Languages and Systems (TOPLAS)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
On a graph-theoretical model for cyclic register allocation
Discrete Applied Mathematics
Fitness landscapes and memetic algorithm design
New ideas in optimization
New methods to color the vertices of a graph
Communications of the ACM
Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, Workshop, October 11-13, 1993
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Fundamentals of Computer Alori
Fundamentals of Computer Alori
Graph Coloring with Adaptive Evolutionary Algorithms
Journal of Heuristics
A New Genetic Local Search Algorithm for Graph Coloring
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Neutrality and self-adaptation
Natural Computing: an international journal
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
A survey of local search methods for graph coloring
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
Computers and Operations Research
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
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
Graph Theory
A Metaheuristic Approach for the Vertex Coloring Problem
INFORMS Journal on Computing
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Hybrid metaheuristics in combinatorial optimization: A survey
Applied Soft Computing
An analysis of heuristics for vertex colouring
SEA'10 Proceedings of the 9th international conference on Experimental Algorithms
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A Study of Tabu Search for Coloring Random 3-Colorable Graphs Around the Phase Transition
International Journal of Applied Metaheuristic Computing
A hierarchical parallel genetic approach for the graph coloring problem
Applied Intelligence
Hi-index | 0.00 |
This paper proposes a hybrid self-adaptive evolutionary algorithm for graph coloring that is hybridized with the following novel elements: heuristic genotype-phenotype mapping, a swap local search heuristic, and a neutral survivor selection operator. This algorithm was compared with the evolutionary algorithm with the SAW method of Eiben et al., the Tabucol algorithm of Hertz and de Werra, and the hybrid evolutionary algorithm of Galinier and Hao. The performance of these algorithms were tested on a test suite consisting of randomly generated 3-colorable graphs of various structural features, such as graph size, type, edge density, and variability in sizes of color classes. Furthermore, the test graphs were generated including the phase transition where the graphs are hard to color. The purpose of the extensive experimental work was threefold: to investigate the behavior of the tested algorithms in the phase transition, to identify what impact hybridization with the DSatur traditional heuristic has on the evolutionary algorithm, and to show how graph structural features influence the performance of the graph-coloring algorithms. The results indicate that the performance of the hybrid self-adaptive evolutionary algorithm is comparable with, or better than, the performance of the hybrid evolutionary algorithm which is one of the best graph-coloring algorithms today. Moreover, the fact that all the considered algorithms performed poorly on flat graphs confirms that graphs of this type are really the hardest to color.