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» Computing Cartograms with Optimal Complexity
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107
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GECCO
2009
Springer
159views Optimization» more  GECCO 2009»
15 years 5 months ago
Bayesian network structure learning using cooperative coevolution
We propose a cooperative-coevolution – Parisian trend – algorithm, IMPEA (Independence Model based Parisian EA), to the problem of Bayesian networks structure estimation. It i...
Olivier Barrière, Evelyne Lutton, Pierre-He...
90
Voted
ASPDAC
2007
ACM
88views Hardware» more  ASPDAC 2007»
15 years 4 months ago
Voltage Island Generation under Performance Requirement for SoC Designs
Using multiple supply voltages on a SoC design is an efficient way to achieve low power. However, it may lead to a complex power network and a huge number of level shifters if we j...
Wai-Kei Mak, Jr-Wei Chen
ECAI
2008
Springer
15 years 2 months ago
Structure Learning of Markov Logic Networks through Iterated Local Search
Many real-world applications of AI require both probability and first-order logic to deal with uncertainty and structural complexity. Logical AI has focused mainly on handling com...
Marenglen Biba, Stefano Ferilli, Floriana Esposito
GECCO
2008
Springer
158views Optimization» more  GECCO 2008»
15 years 1 months ago
Structure and parameter estimation for cell systems biology models
In this work we present a new methodology for structure and parameter estimation in cell systems biology modelling. Our modelling framework is based on P systems, an unconl comput...
Francisco José Romero-Campero, Hongqing Cao...
SODA
2010
ACM
190views Algorithms» more  SODA 2010»
15 years 10 months ago
One-Counter Markov Decision Processes
We study the computational complexity of some central analysis problems for One-Counter Markov Decision Processes (OC-MDPs), a class of finitely-presented, countable-state MDPs. O...
Tomas Brazdil, Vaclav Brozek, Kousha Etessami, Ant...