Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Tabu Search
A Comparison of Parallel and Sequential Niching Methods
Proceedings of the 6th International Conference on Genetic Algorithms
A New Genetic Local Search Algorithm for Graph Coloring
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Diversity-Guided Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Scatter Search for Graph Coloring
Selected Papers from the 5th European Conference on Artificial Evolution
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
MA|PM: memetic algorithms with population management
Computers and Operations Research
A survey of local search methods for graph coloring
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
A graph coloring heuristic using partial solutions and a reactive tabu scheme
Computers and Operations Research
Searching for diverse, cooperative populations with genetic algorithms
Evolutionary Computation
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
A search space "cartography" for guiding graph coloring heuristics
Computers and Operations Research
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Coloring large graphs based on independent set extraction
Computers and Operations Research
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Safe lower bounds for graph coloring
IPCO'11 Proceedings of the 15th international conference on Integer programming and combinatoral optimization
Graph coloring with a distributed hybrid quantum annealing algorithm
KES-AMSTA'11 Proceedings of the 5th KES international conference on Agent and multi-agent systems: technologies and applications
An effective heuristic algorithm for sum coloring of graphs
Computers and Operations Research
A wide-ranging computational comparison of high-performance graph colouring algorithms
Computers and Operations Research
Note: Quantum annealing of the graph coloring problem
Discrete Optimization
On the efficiency of an order-based representation in the clique covering problem
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Improving the extraction and expansion method for large graph coloring
Discrete Applied Mathematics
Memetic search for the max-bisection problem
Computers and Operations Research
A memetic approach for the max-cut problem
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
A memetic algorithm for the Minimum Sum Coloring Problem
Computers and Operations Research
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We present a diversity-oriented hybrid evolutionary approach for the graph coloring problem. This approach is based on both generally applicable strategies and specifically tailored techniques. Particular attention is paid to ensuring population diversity by carefully controlling spacing among individuals. Using a distance measure between potential solutions, the general population management strategy decides whether an offspring should be accepted in the population, which individual needs to be replaced and when mutation is applied. Furthermore, we introduce a special grouping-based multi-parent crossover operator which relies on several relevant features to identify meaningful building blocks for offspring construction. The proposed approach can be generally characterized as ''well-informed'', in the sense that the design of each component is based on the most pertinent information which is identified by both experimental observation and careful analysis of the given problem. The resulting algorithm proves to be highly competitive when it is applied on the whole set of the DIMACS benchmark graphs.