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» Local and Global Comparison of Continuous Functions
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EOR
2010
116views more  EOR 2010»
13 years 6 months ago
Speeding up continuous GRASP
Continuous GRASP (C-GRASP) is a stochastic local search metaheuristic for finding cost-efficient solutions to continuous global optimization problems subject to box constraints (Hi...
Michael J. Hirsch, Panos M. Pardalos, Mauricio G. ...
SIAMADS
2010
122views more  SIAMADS 2010»
13 years 28 days ago
Local/Global Analysis of the Stationary Solutions of Some Neural Field Equations
Neural or cortical fields are continuous assemblies of mesoscopic models, also called neural masses, of neural populations that are fundamental in the modeling of macroscopic parts...
Romain Veltz, Olivier D. Faugeras
MA
2010
Springer
135views Communications» more  MA 2010»
13 years 4 months ago
Nonparametric comparison of regression functions
In this work we provide a new methodology for comparing regression functions m1 and m2 from two samples. Since apart from smoothness no other (parametric) assumptions are required...
Ramidha Srihera, Winfried Stute
CEC
2010
IEEE
13 years 4 months ago
Comparison of GA and PSO performance in parameter estimation of microbial growth models: A case-study using experimental data
In this work we examined the performance of two evolutionary algorithms, a genetic algorithm (GA) and particle swarm optimization (PSO), in the estimation of the parameters of a mo...
Dulce Calcada, Agostinho Rosa, Luis C. Duarte, Vit...
AAAI
1998
13 years 7 months ago
Applying Online Search Techniques to Continuous-State Reinforcement Learning
In this paper, we describe methods for e ciently computing better solutions to control problems in continuous state spaces. We provide algorithms that exploit online search to boo...
Scott Davies, Andrew Y. Ng, Andrew W. Moore