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GECCO
2009
Springer
124views Optimization» more  GECCO 2009»
15 years 2 months ago
Reinforcement learning for games: failures and successes
We apply CMA-ES, an evolution strategy with covariance matrix adaptation, and TDL (Temporal Difference Learning) to reinforcement learning tasks. In both cases these algorithms se...
Wolfgang Konen, Thomas Bartz-Beielstein
92
Voted
GECCO
2009
Springer
148views Optimization» more  GECCO 2009»
15 years 1 months ago
Tracking multiple objects in non-stationary video
One of the key problems in computer vision and pattern recognition is tracking. Multiple objects, occlusion, and tracking moving objects using a moving camera are some of the chal...
Hoang Nguyen, Bir Bhanu
GECCO
2009
Springer
148views Optimization» more  GECCO 2009»
14 years 8 months ago
Genetic programming for quantitative stock selection
We provide an overview of using genetic programming (GP) to model stock returns. Our models employ GP terminals (model decision variables) that are financial factors identified by...
Ying L. Becker, Una-May O'Reilly
GECCO
2009
Springer
113views Optimization» more  GECCO 2009»
15 years 4 months ago
Environmental robustness in multi-agent teams
Evolution has proven to be an effective method of training heterogeneous multi-agent teams of autonomous agents to explore unknown environments. Autonomous, heterogeneous agents ...
Terence Soule, Robert B. Heckendorn
100
Voted
GECCO
2009
Springer
166views Optimization» more  GECCO 2009»
15 years 4 months ago
Genetic programming in the wild: evolving unrestricted bytecode
We describe a methodology for evolving Java bytecode, enabling the evolution of extant, unrestricted Java programs, or programs in other languages that compile to Java bytecode. B...
Michael Orlov, Moshe Sipper