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IWANN
1999
Springer
14 years 12 days ago
Using Temporal Neighborhoods to Adapt Function Approximators in Reinforcement Learning
To avoid the curse of dimensionality, function approximators are used in reinforcement learning to learn value functions for individual states. In order to make better use of comp...
R. Matthew Kretchmar, Charles W. Anderson
CEC
2010
IEEE
12 years 11 months ago
Revisiting Evolutionary Information Filtering
— Adaptive Information Filtering seeks a solution to the problem of information overload through a tailored representation of the user’s interests, called user profile, which ...
Nikolaos Nanas, Stefanos Kodovas, Manolis Vavalis
DATE
2009
IEEE
137views Hardware» more  DATE 2009»
14 years 2 months ago
Adaptive prefetching for shared cache based chip multiprocessors
Abstract—Chip multiprocessors (CMPs) present a unique scenario for software data prefetching with subtle tradeoffs between memory bandwidth and performance. In a shared L2 based ...
Mahmut T. Kandemir, Yuanrui Zhang, Ozcan Ozturk

Publication
285views
14 years 23 hour ago
Center of mass encoding: a self-adaptive representation with adjustable redundancy for real-valued parameters
In this paper we describe a new class of representations for realvalued parameters called Center of Mass Encoding (CoME). CoME is based on variable length strings, it is self-adap...
Claudio Mattiussi, Peter Dürr, Dario Floreano
EMO
2009
Springer
159views Optimization» more  EMO 2009»
14 years 2 months ago
Recombination for Learning Strategy Parameters in the MO-CMA-ES
The multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) is a variable-metric algorithm for real-valued vector optimization. It maintains a parent population...
Thomas Voß, Nikolaus Hansen, Christian Igel