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» From Algorithmic to Subjective Randomness
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138
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COMPGEOM
1994
ACM
15 years 7 months ago
Constructing Levels in Arrangements and Higher Order Voronoi Diagrams
We give simple randomized incremental algorithms for computing the k-level in an arrangement of n lines in the plane or in an arrangement of n planes in R3. The expected running ti...
Pankaj K. Agarwal, Mark de Berg, Jirí Matou...
116
Voted
GECCO
2006
Springer
122views Optimization» more  GECCO 2006»
15 years 7 months ago
Genetic algorithms are suitable for driving microbial ecosystems in desirable directions
The behavior of natural, biological ecosystems is for a large part determined by environmental conditions. It should therefore be possible to experimentally manipulate such condit...
Frederik P. J. Vandecasteele, Thomas F. Hess, Rona...
STOC
2012
ACM
221views Algorithms» more  STOC 2012»
13 years 6 months ago
From query complexity to computational complexity
We consider submodular optimization problems, and provide a general way of translating oracle inapproximability results arising from the symmetry gap technique to computational co...
Shahar Dobzinski, Jan Vondrák
GECCO
2008
Springer
117views Optimization» more  GECCO 2008»
15 years 4 months ago
Is "best-so-far" a good algorithmic performance metric?
In evolutionary computation, experimental results are commonly analyzed using an algorithmic performance metric called best-so-far. While best-so-far can be a useful metric, its u...
Nathaniel P. Troutman, Brent E. Eskridge, Dean F. ...
AAAI
2011
14 years 3 months ago
An Online Spectral Learning Algorithm for Partially Observable Nonlinear Dynamical Systems
Recently, a number of researchers have proposed spectral algorithms for learning models of dynamical systems—for example, Hidden Markov Models (HMMs), Partially Observable Marko...
Byron Boots, Geoffrey J. Gordon