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AAAI
1998
15 years 5 months ago
Optimal 2D Model Matching Using a Messy Genetic Algorithm
A Messy Genetic Algorithm is customized toflnd'optimal many-to-many matches for 2D line segment models. The Messy GA is a variant upon the Standard Genetic Algorithm in which...
J. Ross Beveridge
TON
2008
120views more  TON 2008»
15 years 3 months ago
Centralized and distributed algorithms for routing and weighted max-min fair bandwidth allocation
Given a set of demands between pairs of nodes, we examine the traffic engineering problem of flow routing and fair bandwidth allocation where flows can be split to multiple paths ...
Miriam Allalouf, Yuval Shavitt
ICML
2005
IEEE
16 years 5 months ago
Active learning for Hidden Markov Models: objective functions and algorithms
Hidden Markov Models (HMMs) model sequential data in many fields such as text/speech processing and biosignal analysis. Active learning algorithms learn faster and/or better by cl...
Brigham Anderson, Andrew Moore
GECCO
2006
Springer
185views Optimization» more  GECCO 2006»
15 years 8 months ago
Robot gaits evolved by combining genetic algorithms and binary hill climbing
In this paper an evolutionary algorithm is used for evolving gaits in a walking biped robot controller. The focus is fast learning in a real-time environment. An incremental appro...
Lena Mariann Garder, Mats Erling Høvin
COMPGEOM
2008
ACM
15 years 6 months ago
Self-improving algorithms for delaunay triangulations
We study the problem of two-dimensional Delaunay triangulation in the self-improving algorithms model [1]. We assume that the n points of the input each come from an independent, ...
Kenneth L. Clarkson, C. Seshadhri