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GECCO
2010
Springer
169views Optimization» more  GECCO 2010»
15 years 6 months ago
Robust symbolic regression with affine arithmetic
We use affine arithmetic to improve both the performance and the robustness of genetic programming for symbolic regression. During evolution, we use affine arithmetic to analyze e...
Cassio Pennachin, Moshe Looks, João A. de V...
GECCO
2004
Springer
15 years 8 months ago
Upper Bounds on the Time and Space Complexity of Optimizing Additively Separable Functions
Abstract. We present upper bounds on the time and space complexity of finding the global optimum of additively separable functions, a class of functions that has been studied exten...
Matthew J. Streeter
GECCO
2006
Springer
171views Optimization» more  GECCO 2006»
15 years 6 months ago
Evolving ensemble of classifiers in random subspace
Various methods for ensemble selection and classifier combination have been designed to optimize the results of ensembles of classifiers. Genetic algorithm (GA) which uses the div...
Albert Hung-Ren Ko, Robert Sabourin, Alceu de Souz...
GECCO
2010
Springer
249views Optimization» more  GECCO 2010»
15 years 5 months ago
Towards improved dispatching rules for complex shop floor scenarios: a genetic programming approach
Developing dispatching rules for manufacturing systems is a tedious process, which is time- and cost-consuming. Since there is no good general rule for different scenarios and ob...
Torsten Hildebrandt, Jens Heger, Bernd Scholz-Reit...
GECCO
2011
Springer
276views Optimization» more  GECCO 2011»
14 years 6 months ago
Evolution of reward functions for reinforcement learning
The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In so...
Scott Niekum, Lee Spector, Andrew G. Barto