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GECCO
2007
Springer
156views Optimization» more  GECCO 2007»
14 years 1 months ago
Techniques for highly multiobjective optimisation: some nondominated points are better than others
The research area of evolutionary multiobjective optimization (EMO) is reaching better understandings of the properties and capabilities of EMO algorithms, and accumulating much e...
David W. Corne, Joshua D. Knowles
CVPR
2010
IEEE
14 years 3 months ago
A Generative Perspective on MRFs in Low-Level Vision
Markov random fields (MRFs) are popular and generic probabilistic models of prior knowledge in low-level vision. Yet their generative properties are rarely examined, while applica...
Uwe Schmidt, Qi Gao, Stefan Roth
GECCO
2006
Springer
134views Optimization» more  GECCO 2006»
13 years 11 months ago
Reference point based multi-objective optimization using evolutionary algorithms
: Evolutionary multi-objective optimization (EMO) methodologies have been amply applied to find a representative set of Pareto-optimal solutions in the past decade and beyond. Alth...
Kalyanmoy Deb, J. Sundar
MFCS
2009
Springer
14 years 1 months ago
A Complexity Dichotomy for Finding Disjoint Solutions of Vertex Deletion Problems
We investigate the computational complexity of a general “compression task” centrally occurring in the recently developed technique of iterative compression for exactly solving...
Michael R. Fellows, Jiong Guo, Hannes Moser, Rolf ...
JCNS
2002
99views more  JCNS 2002»
13 years 7 months ago
Coarse-Grained Reduction and Analysis of a Network Model of Cortical Response: I. Drifting Grating Stimuli
We present a reduction of a large-scale network model of visual cortex developed by McLaughlin, Shapley, Shelley, and Wielaard. The reduction is from many integrate-and-fire neuron...
Michael Shelley, David McLaughlin