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GECCO
2009
Springer
124views Optimization» more  GECCO 2009»
14 years 5 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
GECCO
2010
Springer
172views Optimization» more  GECCO 2010»
14 years 5 months ago
Designing better fitness functions for automated program repair
Evolutionary methods have been used to repair programs automatically, with promising results. However, the fitness function used to achieve these results was based on a few simpl...
Ethan Fast, Claire Le Goues, Stephanie Forrest, We...
EUROGP
2003
Springer
14 years 5 months ago
Evolving Finite State Transducers: Some Initial Explorations
Finite state transducers (FSTs) are finite state machines that map strings in a source domain into strings in a target domain. While there are many reports in the literature of ev...
Simon M. Lucas
EVOW
2005
Springer
14 years 5 months ago
Toward User-Directed Evolution of Sound Synthesis Parameters
Abstract. Experiments are described which use genetic algorithms operating on the parameter settings of an FM synthesizer, with the aim of mimicking known synthesized sounds. The w...
James McDermott, Niall J. L. Griffith, Michael O'N...
CEC
2007
IEEE
14 years 6 months ago
On the adaptation of noise level for stochastic optimization
— This paper deals with the optimization of noisy fitness functions, where the noise level can be reduced by increasing the computational effort. We theoretically investigate th...
Olivier Teytaud, Anne Auger