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EMO
2009
Springer
159views Optimization» more  EMO 2009»
14 years 3 months ago
Recombination for Learning Strategy Parameters in the MO-CMA-ES
The multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) is a variable-metric algorithm for real-valued vector optimization. It maintains a parent population...
Thomas Voß, Nikolaus Hansen, Christian Igel
ICANN
2009
Springer
14 years 3 months ago
Combining Multiple Inputs in HyperNEAT Mobile Agent Controller
In this paper we present neuro-evolution of neural network controllers for mobile agents in a simulated environment. The controller is obtained through evolution of hypercube encod...
Jan Drchal, Ondrej Kapral, Jan Koutník, Mir...
IJCNN
2008
IEEE
14 years 3 months ago
Dynamic logic of phenomena and cognition
—Modeling of complex phenomena such as the mind presents tremendous computational complexity challenges. The neural modeling fields theory (NMF) addresses these challenges in a n...
Boris Kovalerchuk, Leonid I. Perlovsky
EPIA
2007
Springer
14 years 3 months ago
A Genetic Programming Approach to the Generation of Hyper-Heuristics for the Uncapacitated Examination Timetabling Problem
Research in the field of examination timetabling has developed in two directions. The first looks at applying various methodologies to induce examination timetables. The second tak...
Nelishia Pillay, Wolfgang Banzhaf
GECCO
2007
Springer
138views Optimization» more  GECCO 2007»
14 years 3 months ago
Reducing the space-time complexity of the CMA-ES
A limited memory version of the covariance matrix adaptation evolution strategy (CMA-ES) is presented. This algorithm, L-CMA-ES, improves the space and time complexity of the CMA-...
James N. Knight, Monte Lunacek