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GECCO
2007
Springer
187views Optimization» more  GECCO 2007»
14 years 4 months ago
Defining implicit objective functions for design problems
In many design tasks it is difficult to explicitly define an objective function. This paper uses machine learning to derive an objective in a feature space based on selected examp...
Sean Hanna
ATAL
2007
Springer
14 years 4 months ago
Q-value functions for decentralized POMDPs
Planning in single-agent models like MDPs and POMDPs can be carried out by resorting to Q-value functions: a (near-) optimal Q-value function is computed in a recursive manner by ...
Frans A. Oliehoek, Nikos A. Vlassis
FSS
2002
99views more  FSS 2002»
13 years 9 months ago
Extreme physical information and objective function in fuzzy clustering
Fuzzy clustering algorithms have been widely studied and applied in a variety of areas. They become the major techniques7 in cluster analysis. In this paper, we focus on objective...
Michel Ménard, Michel Eboueya
GECCO
2011
Springer
232views Optimization» more  GECCO 2011»
13 years 1 months ago
Mutation rates of the (1+1)-EA on pseudo-boolean functions of bounded epistasis
When the epistasis of the fitness function is bounded by a constant, we show that the expected fitness of an offspring of the (1+1)-EA can be efficiently computed for any point...
Andrew M. Sutton, Darrell Whitley, Adele E. Howe
GECCO
2009
Springer
142views Optimization» more  GECCO 2009»
14 years 2 months ago
Benchmarking the (1+1)-CMA-ES on the BBOB-2009 noisy testbed
We benchmark an independent-restart-(1+1)-CMA-ES on the BBOB-2009 noisy testbed. The (1+1)-CMA-ES is an adaptive stochastic algorithm for the optimization of objective functions d...
Anne Auger, Nikolaus Hansen