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 this paper, on the basis of our proposed approach in [1], we present a rigorous proof for the result that the UMDA with margins (in [1] we merely showed the effectiveness of margins) cannot find the global optimum of the TRAPLEADINGONES problem [2] within polynomial number of generations with a probability that is super-polynomially close

• Type: Conference
• Year: 2009
• Where: CEC
• Authors: Tianshi Chen, Ke Tang, Guoliang Chen, Xin Yao
• Keywords: Artificial Intelligence, CEC 2009, Distribution Algorithms, Rigorous Proof, —univariate Marginal Distribution