DocumentCode :
2919415
Title :
A Bi-level Blocked Estimation of Distribution Algorithm with local search for Maximum Clique Problems
Author :
Zhang, Yan ; Dang, Qun ; Jiang, Zhu ; Huang, Yongxuan
Author_Institution :
Dept. of Electron. & Inf. Engr, Xi´´an JiaoTong Univ., Xi´´an
fYear :
2008
fDate :
1-6 June 2008
Firstpage :
4112
Lastpage :
4117
Abstract :
Maximum clique problem (MCP) is a complicated deceptive problem for estimation of distribution algorithms (EDAs). The univariate EDAs cannot utilize the correlations of the variables and the advanced EDAs perform poor due to the expensive computational cost in building the appropriate probability models. In this paper, by utilizing the special structure of MCP, a new Bi-level Blocked Probability model (BBP) is constructed, which achieves the relationships utilizing in a bivariate probability model at the computational cost of univariate probability model. Integrating promising neighborhood search techniques, a new EDA algorithm, called Bi-level Blocked Estimation of Distribution Algorithm (BBEDA) is proposed for MCP. Comparative experiments on extensive DIMACS Benchmark instances show that the proposed BBEDA can be competitive with the evolutionary algorithm with guided mutation (the best evolutionary algorithm reported so far) in terms of solution quality and computational performance.
Keywords :
computational complexity; distributed algorithms; evolutionary computation; graph theory; probability; bi-level blocked estimation; bi-level blocked probability model; bivariate probability model; distribution algorithms estimation; evolutionary algorithm; local search; maximum clique problems; Evolutionary computation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1822-0
Electronic_ISBN :
978-1-4244-1823-7
Type :
conf
DOI :
10.1109/CEC.2008.4631358
Filename :
4631358
Link To Document :
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