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CP
2005
Springer
13 years 9 months ago
Evolving Variable-Ordering Heuristics for Constrained Optimisation
In this paper we present and evaluate an evolutionary approach for learning new constraint satisfaction algorithms, specifically for MAX-SAT optimisation problems. Our approach of...
Stuart Bain, John Thornton, Abdul Sattar
JCAL
2002
105views more  JCAL 2002»
13 years 7 months ago
Conceptualising work activity for CAL systems design
As computing technology has increasingly become relevant to people's everyday lives, emphasis is being placed on ensuring Computer Assisted Learning (CAL) tools support users ...
D. Mwanza
GECCO
2010
Springer
173views Optimization» more  GECCO 2010»
13 years 11 months ago
The baldwin effect in developing neural networks
The Baldwin Effect is a very plausible, but unproven, biological theory concerning the power of learning to accelerate evolution. Simple computational models in the 1980’s gave...
Keith L. Downing
ATAL
2007
Springer
14 years 1 months ago
Batch reinforcement learning in a complex domain
Temporal difference reinforcement learning algorithms are perfectly suited to autonomous agents because they learn directly from an agent’s experience based on sequential actio...
Shivaram Kalyanakrishnan, Peter Stone
IEEEPACT
2008
IEEE
14 years 2 months ago
Feature selection and policy optimization for distributed instruction placement using reinforcement learning
Communication overheads are one of the fundamental challenges in a multiprocessor system. As the number of processors on a chip increases, communication overheads and the distribu...
Katherine E. Coons, Behnam Robatmili, Matthew E. T...