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GECCO
2006
Springer
175views Optimization» more  GECCO 2006»
14 years 1 months ago
A computational theory of adaptive behavior based on an evolutionary reinforcement mechanism
Two mathematical and two computational theories from the field of human and animal learning are combined to produce a more general theory of adaptive behavior. The cornerstone of ...
J. J. McDowell, Paul L. Soto, Jesse Dallery, Saule...
GECCO
2010
Springer
153views Optimization» more  GECCO 2010»
14 years 1 months ago
Multi-task evolutionary shaping without pre-specified representations
Shaping functions can be used in multi-task reinforcement learning (RL) to incorporate knowledge from previously experienced tasks to speed up learning on a new task. So far, rese...
Matthijs Snel, Shimon Whiteson
ATAL
2010
Springer
13 years 11 months ago
Frequency adjusted multi-agent Q-learning
Multi-agent learning is a crucial method to control or find solutions for systems, in which more than one entity needs to be adaptive. In today's interconnected world, such s...
Michael Kaisers, Karl Tuyls
BIOCOMP
2006
13 years 11 months ago
Using Neural Nets to Estimate Evolutionary Parameters
- The rapid growth in the amount of molecular genetic data being collected will, in many cases, require the development of new analytic methods for the analysis of that data. In th...
Chi-Chiang Lee, Paul Marjoram
HAIS
2008
Springer
13 years 11 months ago
An Evolutionary Approach for Tuning Artificial Neural Network Parameters
The widespread use of artificial neural networks and the difficult work regarding the correct specification (tuning) of parameters for a given problem are the main aspects that mot...
Leandro M. Almeida, Teresa Bernarda Ludermir