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TEC
2002
133views more  TEC 2002»
13 years 7 months ago
Learning and optimization using the clonal selection principle
The clonal selection principle is used to explain the basic features of an adaptive immune response to an antigenic stimulus. It establishes the idea that only those cells that rec...
Leandro Nunes de Castro, Fernando J. Von Zuben
ECAL
2001
Springer
14 years 2 days ago
Pareto Optimality in Coevolutionary Learning
We develop a novel coevolutionary algorithm based upon the concept of Pareto optimality. The Pareto criterion is core to conventional multi-objective optimization (MOO) algorithms....
Sevan G. Ficici, Jordan B. Pollack
CDC
2010
IEEE
106views Control Systems» more  CDC 2010»
13 years 2 months ago
Optimal cross-layer wireless control policies using TD learning
We present an on-line crosslayer control technique to characterize and approximate optimal policies for wireless networks. Our approach combines network utility maximization and ad...
Sean P. Meyn, Wei Chen, Daniel O'Neill
ATAL
2008
Springer
13 years 9 months ago
Adaptive Kanerva-based function approximation for multi-agent systems
In this paper, we show how adaptive prototype optimization can be used to improve the performance of function approximation based on Kanerva Coding when solving largescale instanc...
Cheng Wu, Waleed Meleis
GECCO
2007
Springer
149views Optimization» more  GECCO 2007»
14 years 1 months ago
Informative performance metrics for dynamic optimisation problems
Existing metrics for dynamic optimisation are designed primarily to rate an algorithm’s overall performance. These metrics show whether one algorithm is better than another, but...
Stefan Bird, Xiaodong Li