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An evolutionary algorithm with guided mutation for the maximum clique problem

delete2005-04-01
delete179
PRE
AI
Qingfu Zhang cover
Qingfu Zhang (Qingfu Zhang)
J
Jianyong Sun
E
Edward Tsang
DOI:10.1109/TEVC.2004.840835delete
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Abstract

Abstract

En 中文
Estimation of distribution algorithms sample new solutions (offspring) from a probability model which characterizes the distribution of promising solutions in the search space at each generation. The location information of solutions found so far (i.e., the actual positions of these solutions in the search space) is not directly used for generating offspring in most existing estimation of distribution algorithms. This paper introduces a new operator, called guided mutation. Guided mutation generates offspring through combination of global statistical information and the location information of solutions found so far. An evolutionary algorithm with guided mutation (EA/G) for the maximum clique problem is proposed in this paper. Besides guided mutation, EA/G adopts a strategy for searching different search areas in different search phases. Marchiori's heuristic is applied to each new solution to produce a maximal clique in EA/G. Experimental results show that EA/G outperforms the heuristic genetic algorithm of Marchiori (the best evolutionary algorithm reported so far) and a MIMIC algorithm on DIMACS benchmark graphs.
Keywords:
estimation of distribution algorithms
evolutionary algorithm
guided mutation
heuristics
hybrid genetic algorithm
maximum clique problem (MCP)

Journal

IEEE Transactions on Evolutionary Computation cover
IEEE Transactions on Evolutionary Computation
IF:
12
Papers:
1.9K
Citations:
2.4W

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