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GECCO
2007
Springer
187views Optimization» more  GECCO 2007»
16 years 27 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
2007
Springer
162views Optimization» more  GECCO 2007»
16 years 27 days ago
A multi-objective approach to discover biclusters in microarray data
The main motivation for using a multi–objective evolutionary algorithm for finding biclusters in gene expression data is motivated by the fact that when looking for biclusters ...
Federico Divina, Jesús S. Aguilar-Ruiz
GECCO
2007
Springer
168views Optimization» more  GECCO 2007»
16 years 27 days ago
Sex and death: towards biologically inspired heuristics for constraint handling
Constrained continuous optimization is still an interesting field of research. Many heuristics have been proposed in the last decade. Most of them are based on penalty functions....
Oliver Kramer, Stephan Brügger, Dejan Lazovic
GECCO
2007
Springer
276views Optimization» more  GECCO 2007»
16 years 27 days ago
Meta-optimizing semantic evolutionary search
I present MOSES (meta-optimizing semantic evolutionary search), a new probabilistic modeling (estimation of distribution) approach to program evolution. Distributions are not esti...
Moshe Looks
161
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GECCO
2007
Springer
186views Optimization» more  GECCO 2007»
16 years 27 days ago
ICSPEA: evolutionary five-axis milling path optimisation
ICSPEA is a novel multi-objective evolutionary algorithm which integrates aspects from the powerful variation operators of the Covariance Matrix Adaptation Evolution Strategy (CMA...
Jörn Mehnen, Rajkumar Roy, Petra Kersting, To...