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CEC
2009
IEEE
13 years 11 months ago
Rigorous time complexity analysis of Univariate Marginal Distribution Algorithm with margins
—Univariate Marginal Distribution Algorithms (UMDAs) are a kind of Estimation of Distribution Algorithms (EDAs) which do not consider the dependencies among the variables. In thi...
Tianshi Chen, Ke Tang, Guoliang Chen, Xin Yao
GECCO
2005
Springer
154views Optimization» more  GECCO 2005»
13 years 10 months ago
Genetic drift in univariate marginal distribution algorithm
Like Darwinian-type genetic algorithms, there also exists genetic drift in Univariate Marginal Distribution Algorithm (UMDA). Since the universal analysis of genetic drift in UMDA...
Yi Hong, Qingsheng Ren, Jin Zeng
CEC
2007
IEEE
13 years 11 months ago
On the analysis of average time complexity of estimation of distribution algorithms
— Estimation of Distribution Algorithm (EDA) is a well-known stochastic optimization technique. The average time complexity is a crucial criterion that measures the performance o...
Tianshi Chen, Ke Tang, Guoliang Chen, Xin Yao
TEC
2010
173views more  TEC 2010»
12 years 11 months ago
Analysis of Computational Time of Simple Estimation of Distribution Algorithms
Estimation of distribution algorithms (EDAs) are widely used in stochastic optimization. Impressive experimental results have been reported in the literature. However, little work ...
Tianshi Chen, Ke Tang, Guoliang Chen, Xin Yao
CEC
2009
IEEE
13 years 11 months ago
When is an estimation of distribution algorithm better than an evolutionary algorithm?
—Despite the wide-spread popularity of estimation of distribution algorithms (EDAs), there has been no theoretical proof that there exist optimisation problems where EDAs perform...
Tianshi Chen, Per Kristian Lehre, Ke Tang, Xin Yao