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ECAL
2005
Springer
15 years 5 months ago
The Quantitative Law of Effect is a Robust Emergent Property of an Evolutionary Algorithm for Reinforcement Learning
An evolutionary reinforcement-learning algorithm, the operation of which was not associated with an optimality condition, was instantiated in an artificial organism. The algorithm ...
J. J. McDowell, Zahra Ansari
88
Voted
SIAMIS
2008
116views more  SIAMIS 2008»
14 years 11 months ago
Quantitative Object Reconstruction Using Abel Transform X-Ray Tomography and Mixed Variable Optimization
This paper introduces a new approach to the problem of quantitative reconstruction of an object from few radiographic views. A mixed variable programming problem is formulated in ...
Mark A. Abramson, Thomas J. Asaki, J. E. Dennis, K...
79
Voted
CDC
2008
IEEE
114views Control Systems» more  CDC 2008»
15 years 1 months ago
Dynamic test selection for reconfigurable diagnosis
Abstract-- Detecting and isolating multiple faults is a computationally intense task which typically consists of computing a set of tests, and then computing the diagnoses based on...
Mattias Krysander, Fredrik Heintz, Jacob Roll, Eri...
104
Voted
ISNN
2010
Springer
15 years 4 months ago
Multiattribute Bayesian Preference Elicitation with Pairwise Comparison Queries
Preference elicitation (PE) is an important component of interactive decision support systems that aim to make optimal recommendations to users by actively querying their preferen...
Shengbo Guo, Scott Sanner
GECCO
2004
Springer
15 years 5 months ago
Modeling Selection Intensity for Toroidal Cellular Evolutionary Algorithms
We present quantitative models for the selection pressure of cellular evolutionary algorithms structured in two dimensional regular lattices. We derive models based on probabilisti...
Mario Giacobini, Enrique Alba, Andrea Tettamanzi, ...