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» A Guided Monte Carlo Approach to Optimization Problems
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AAAI
2007
15 years 9 days ago
Acquiring Visibly Intelligent Behavior with Example-Guided Neuroevolution
Much of artificial intelligence research is focused on devising optimal solutions for challenging and well-defined but highly constrained problems. However, as we begin creating...
Bobby D. Bryant, Risto Miikkulainen
INFOCOM
2010
IEEE
14 years 8 months ago
Queuing Analysis in Multichannel Cognitive Spectrum Access: A Large Deviation Approach
Abstract—The queueing performance of a (secondary) cognitive user is investigated for a hierarchical network where there are N independent and identical primary users. Each prima...
Amine Laourine, Shiyao Chen, Lang Tong
EOR
2007
77views more  EOR 2007»
14 years 10 months ago
Solving the short-term electrical generation scheduling problem by an adaptive evolutionary approach
In this paper, we introduce an adaptive evolutionary approach to solve the short-term electrical generation scheduling problem (STEGS). The STEGS is a hard constraint satisfaction...
Jorge Maturana, María-Cristina Riff
GECCO
2008
Springer
148views Optimization» more  GECCO 2008»
14 years 11 months ago
Accelerating convergence using rough sets theory for multi-objective optimization problems
We propose the use of rough sets theory to improve the first approximation provided by a multi-objective evolutionary algorithm and retain the nondominated solutions using a new ...
Luis V. Santana-Quintero, Carlos A. Coello Coello
VLDB
2009
ACM
182views Database» more  VLDB 2009»
15 years 10 months ago
Guessing the extreme values in a data set: a Bayesian method and its applications
For a largenumber of data management problems, it would be very useful to be able to obtain a few samples from a data set, and to use the samples to guess the largest (or smallest)...
Mingxi Wu, Chris Jermaine