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en:mcp [2012/08/03 16:54] – dangduccen:mcp [2012/08/03 18:06] – dangducc
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 === New approach === === New approach ===
  
-Subgraph Extraction Approach (SEA) for Solving MCP consists of first extracting a particular subgraph from the original one, then finding the maximum clique in this subgraph. Particular subgraphs are chosen such that the MCP can be solved in polynomial time. This approach was early developed in [1, 3] with triangulated subgraphs. The authors proposed also associated heuristic approaches but these algorithms had never been tested on recent MCP benchmarks like the DIMACS one.+Subgraph Extraction Approach (SEA) for solving MCP consists of first extracting a particular subgraph from the original one, then finding the maximum clique in this subgraph. Particular subgraphs are chosen such that the MCP can be solved in polynomial time. This approach was early developed in [1, 3] with triangulated subgraphs. The authors proposed also associated heuristic approaches but these algorithms had never been tested on recent MCP benchmarks like the DIMACS one.
  
 Our new idea is follow: the extraction process will not only be influenced by the original graph but also by an order of vertices, this eventually accelerates the extraction. Therefore, if there exists an order of vertices which return the maximum clique of the original graph after the extraction, the MCP can be seen as permutation problem and can be solved using evolutionary algorithms (EA). Particularly for genetic algorithm, this method actually removes the need of repair steps after each crossover operator in genetic algorithm designs for MCP using binary representations. Our new idea is follow: the extraction process will not only be influenced by the original graph but also by an order of vertices, this eventually accelerates the extraction. Therefore, if there exists an order of vertices which return the maximum clique of the original graph after the extraction, the MCP can be seen as permutation problem and can be solved using evolutionary algorithms (EA). Particularly for genetic algorithm, this method actually removes the need of repair steps after each crossover operator in genetic algorithm designs for MCP using binary representations.

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en/mcp.txt · Last modified: 2012/08/10 04:00 by dangducc
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