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75
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CEC
2009
IEEE
15 years 6 months ago
Comparing parameter tuning methods for evolutionary algorithms
Abstract— Tuning the parameters of an evolutionary algorithm (EA) to a given problem at hand is essential for good algorithm performance. Optimizing parameter values is, however,...
Selmar K. Smit, A. E. Eiben
PPSN
2004
Springer
15 years 5 months ago
Optimization via Parameter Mapping with Genetic Programming
Abstract. This paper describes a new approach for parameter optimization that uses a novel representation for the parameters to be optimized. By using genetic programming, the new ...
João Carlos Figueira Pujol, Riccardo Poli
90
Voted
ICPR
2008
IEEE
16 years 29 days ago
Exploiting qualitative domain knowledge for learning Bayesian network parameters with incomplete data
When a large amount of data are missing, or when multiple hidden nodes exist, learning parameters in Bayesian networks (BNs) becomes extremely difficult. This paper presents a lea...
Qiang Ji, Wenhui Liao
ICASSP
2010
IEEE
14 years 12 months ago
Statistical Resolution Limit for multiple parameters of interest and for multiple signals
The concept of Statistical Resolution Limit (SRL), which is defined as the minimal separation to resolve two closely spaced signals, is an important tool to quantify performance ...
Mohammed Nabil El Korso, Rémy Boyer, Alexan...
JCNS
2010
103views more  JCNS 2010»
14 years 6 months ago
Efficient computation of the maximum a posteriori path and parameter estimation in integrate-and-fire and more general state-spa
A number of important data analysis problems in neuroscience can be solved using state-space models. In this article, we describe fast methods for computing the exact maximum a pos...
Shinsuke Koyama, Liam Paninski