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113
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SAC
2009
ACM
15 years 9 months ago
A gradient oriented recombination scheme for evolution strategies
This paper proposes a novel recombination scheme for evolutionary algorithms, which can guide the new population generation towards the maximum increase of the objective function....
Haifeng Chen, Guofei Jiang
124
Voted
SEFM
2003
IEEE
15 years 7 months ago
From Requirements to Design: Formalizing the Key Steps
Despite the advances in software engineering since 1968, current methods for going from a set of functional requirements to a design are not as direct, repeatable and constructive...
R. Geoff Dromey
ML
2007
ACM
106views Machine Learning» more  ML 2007»
15 years 1 months ago
Surrogate maximization/minimization algorithms and extensions
Abstract Surrogate maximization (or minimization) (SM) algorithms are a family of algorithms that can be regarded as a generalization of expectation-maximization (EM) algorithms. A...
Zhihua Zhang, James T. Kwok, Dit-Yan Yeung
148
Voted
ISNN
2010
Springer
15 years 26 days ago
Particle Swarm Optimization Based Learning Method for Process Neural Networks
Abstract. This paper proposes a new learning method for process neural networks (PNNs) based on the Gaussian mixture functions and particle swarm optimization (PSO), called PSO-LM....
Kun Liu, Ying Tan, Xingui He
TMI
2010
155views more  TMI 2010»
14 years 9 months ago
3D Forward and Back-Projection for X-Ray CT Using Separable Footprints
The greatest impediment to practical adoption of iterative methods for X-ray CT is the computation burden of cone-beam forward and back-projectors. Moreover, forward and back-proje...
Yong Long, Jeffrey A. Fessler, James M. Balter