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Evolutionary algorithm with the guided mutation for the maximum clique problem (2005)
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| Venue: | IEEE Transactions on Evolutionary Computation |
| Citations: | 42 - 15 self |
BibTeX
@ARTICLE{Zhang05evolutionaryalgorithm,
author = {Qingfu Zhang and Jianyong Sun and Edward Tsang},
title = {Evolutionary algorithm with the guided mutation for the maximum clique problem},
journal = {IEEE Transactions on Evolutionary Computation},
year = {2005},
volume = {9},
pages = {2005}
}
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Abstract
Abstract—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. Index Terms—Estimation of distribution algorithms, evolutionary algorithm, guided mutation, heuristics, hybrid genetic algorithm, maximum clique problem (MCP). I.
Keyphrases
evolutionary algorithm maximum clique problem guided mutation distribution algorithm search space new solution location information mutation generates different search phase abstract estimation hybrid genetic algorithm promising solution global statistical information new operator heuristic genetic algorithm experimental result probability model maximal clique mimic algorithm different search area actual position index term estimation dimacs benchmark graph marchiori heuristic
