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GECCO
2011
Springer
276views Optimization» more  GECCO 2011»
13 years 5 days 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
GECCO
2007
Springer
185views Optimization» more  GECCO 2007»
14 years 2 months ago
An informed convergence accelerator for evolutionary multiobjective optimiser
A novel optimisation accelerator deploying neural network predictions and objective space direct manipulation strategies is presented. The concept of directing the search through ...
Salem F. Adra, Ian Griffin, Peter J. Fleming
GECCO
2005
Springer
122views Optimization» more  GECCO 2005»
14 years 2 months ago
An enhanced GA to improve the search process reliability in tuning of control systems
Evolutionary Algorithms (EAs) have been largely applied to optimisation and synthesis of controllers. In spite of several successful applications and competitive solutions, the st...
Andrea Soltoggio
CGO
2011
IEEE
13 years 14 days ago
Language and compiler support for auto-tuning variable-accuracy algorithms
—Approximating ideal program outputs is a common technique for solving computationally difficult problems, for adhering to processing or timing constraints, and for performance ...
Jason Ansel, Yee Lok Wong, Cy P. Chan, Marek Olsze...
CEC
2010
IEEE
13 years 9 months ago
Linear antenna array synthesis using fitness-adaptive differential evolution algorithm
-- Design of non-uniform linear antenna arrays is one of the most important electromagnetic optimization problems of current interest. In this article, an adaptive Differential Evo...
Aritra Chowdhury, Ritwik Giri, Arnob Ghosh, Swagat...