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ATAL
2005
Springer
14 years 1 months ago
Improving reinforcement learning function approximators via neuroevolution
Reinforcement learning problems are commonly tackled with temporal difference methods, which use dynamic programming and statistical sampling to estimate the long-term value of ta...
Shimon Whiteson
GECCO
2006
Springer
159views Optimization» more  GECCO 2006»
13 years 11 months ago
Multi-step environment learning classifier systems applied to hyper-heuristics
Heuristic Algorithms (HA) are very widely used to tackle practical problems in operations research. They are simple, easy to understand and inspire confidence. Many of these HAs a...
Javier G. Marín-Blázquez, Sonia Schu...
IPPS
2007
IEEE
14 years 1 months ago
Optimizing Sorting with Machine Learning Algorithms
The growing complexity of modern processors has made the development of highly efficient code increasingly difficult. Manually developing highly efficient code is usually expen...
Xiaoming Li, María Jesús Garzar&aacu...
ATAL
2008
Springer
13 years 9 months ago
MB-AIM-FSI: a model based framework for exploiting gradient ascent multiagent learners in strategic interactions
Future agent applications will increasingly represent human users autonomously or semi-autonomously in strategic interactions with similar entities. Hence, there is a growing need...
Doran Chakraborty, Sandip Sen
RECOMB
2002
Springer
14 years 7 months ago
String barcoding: uncovering optimal virus signatures
There are many critical situations when one needs to rapidly identify an unidentified pathogen from among a given set of previously sequenced pathogens. DNA or RNA hybridization c...
Sam Rash, Dan Gusfield