Titlebook: Advances in Soft Computing; 10th Mexican Interna Ildar Batyrshin,Grigori Sidorov Conference proceedings 2011 Springer-Verlag GmbH Berlin He

抗原

Global Optimization with the Gaussian Polytree EDAm are based on information theoretic concepts such as mutual information and conditional mutual information. The proposed Gaussian polytree estimation of distribution algorithm is applied to a set of benchmark functions. The experimental results show that the approach is robust, comparisons are provided.

世俗

打擊

演繹

Asseverate

Christoph Schorn,Andre Guntoro,Gerd Ascheidan inspired on Particle Swarm Optimization learning method. The theoretical results come from necessities in a real case study that is also presented, showing then the practical approach of the proposal, where new issues were obtained but also real problems were solved.

小平面

Lecture Notes in Computer Sciencegh a hyperheuristic. HH_AdaNAS performs adaptive control through the hyperheuristic considering SQRP local conditions. The experimental results show that HH_AdaNAS, incorporating the techniques of parameters tuning with hyperheuristics, increases its performance by 2.42% compared with the algorithms to solve SQRP found in literature.

Bridle

Computer Safety, Reliability, and Securityo, and taking into account the activity of the users, size of area, quantity of lights, and power. It was found that the GA provides an optimal configuration (according to the user’s needs), and this result was consistent with Wilcoxon’s Test.

magenta

0302-9743 nspired algorithms; data mining; neural networks and hybrid intelligent systems; and computer vision and image processing.978-3-642-25329-4978-3-642-25330-0Series ISSN 0302-9743 Series E-ISSN 1611-3349

偏離

Lecture Notes in Computer Scienceobjective is to use all this information to estimate the probability of failure of the entire the complex system. In this paper, we describe a new efficient method for such estimation based on Cauchy deviates.

語言學(xué)

Nikita Bhardwaj Haupt,Peter Liggesmeyerhis paper we introduce a new ensemble learning based on the ant colony clustering algorithm. Experimental results on some real-world datasets are presented to demonstrate the effectiveness of the proposed method in generating the final partition.