Solving the maximum clique problem using a tabu search approach
Annals of Operations Research - Special issue on Tabu search
Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Variable neighborhood search for the maximum clique
Discrete Applied Mathematics - The fourth international colloquium on graphs and optimisation (GO-IV)
On complexity of optimal recombination for binary representations of solutions
Evolutionary Computation
Experimental comparison of two evolutionary algorithms for the independent set problem
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Very large-scale neighborhood search techniques in timetabling problems
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
IEEE Transactions on Wireless Communications
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
On complexity of the optimal recombination for the travelling salesman problem
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
Subgraph extraction and metaheuristics for the maximum clique problem
Journal of Heuristics
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In Balas and Niehaus (1996), we have developed a heuristic for generating large cliques in anarbitrary graph, by repeatedly taking two cliques and finding amaximum clique in the subgraph induced by the union of theirvertex sets, an operation executable in polynomial time throughbipartite matching in the complement of the subgraph. Aggarwal, Orlin and Tai (1997) recognized thatthe latter operation can be embedded into the framework of agenetic algorithm as an optimized crossover operation. Inspiredby their approach, we examine variations of each element of thegenetic algorithm—selection, population replacement andmutation—and develop a steady-state genetic algorithm thatperforms better than its competitors on most problems.