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GECCO
2004
Springer
158views Optimization» more  GECCO 2004»
14 years 3 months ago
Adaptively Choosing Neighbourhood Bests Using Species in a Particle Swarm Optimizer for Multimodal Function Optimization
This paper proposes an improved particle swarm optimizer using the notion of species to determine its neighbourhood best values, for solving multimodal optimization problems. In th...
Xiaodong Li
AAAI
2008
14 years 15 days ago
Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...
Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiya...
VLSI
2007
Springer
14 years 4 months ago
An efficient heterogeneous reconfigurable functional unit for an adaptive dynamic extensible processor
Replacing functional units of an extensible processor with reconfigurable functional units enhances performance and flexibility of processors to execute custom instructions. That ...
Arash Mehdizadeh, Behnam Ghavami, Morteza Saheb Za...
NPL
2006
109views more  NPL 2006»
13 years 10 months ago
CB3: An Adaptive Error Function for Backpropagation Training
Effective backpropagation training of multi-layer perceptrons depends on the incorporation of an appropriate error or objective function. Classification-based (CB) error functions ...
Michael Rimer, Tony Martinez
CIG
2005
IEEE
14 years 3 months ago
Adapting Reinforcement Learning for Computer Games: Using Group Utility Functions
AbstractGroup utility functions are an extension of the common team utility function for providing multiple agents with a common reinforcement learning signal for learning cooperat...
Jay Bradley, Gillian Hayes