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ICONIP
2008
15 years 7 months ago
Neural Network Regression for LHF Process Optimization
We present a system for regression using MLP neural networks with hyperbolic tangent functions in the input, hidden and output layer. The activation functions in the input and outp...
Miroslaw Kordos
JGO
2008
53views more  JGO 2008»
15 years 6 months ago
Smoothing by mollifiers. Part II: nonlinear optimization
This article complements the paper [7], where we showed that a compact feasible set of a standard semi-infinite optimization problem can be approximated arbitrarily well by a leve...
Hubertus Th. Jongen, Oliver Stein
GECCO
2007
Springer
187views Optimization» more  GECCO 2007»
16 years 15 days 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
GECCO
2005
Springer
126views Optimization» more  GECCO 2005»
15 years 12 months ago
Not all linear functions are equally difficult for the compact genetic algorithm
Estimation of distribution algorithms (EDAs) try to solve an optimization problem by finding a probability distribution focussed around its optima. For this purpose they conduct ...
Stefan Droste
GECCO
2006
Springer
148views Optimization» more  GECCO 2006»
15 years 10 months ago
A specification-based fitness function for evolutionary testing of object-oriented programs
Encapsulation of states in object-oriented programs hinders the search for test data using evolutionary testing. As client code is oblivious to the internal state of a server obje...
Yoonsik Cheon, Myoung Kim