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136
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GECCO
2007
Springer
187views Optimization» more  GECCO 2007»
15 years 11 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
GECCO
2005
Springer
126views Optimization» more  GECCO 2005»
15 years 11 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 9 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
KDD
1995
ACM
140views Data Mining» more  KDD 1995»
15 years 9 months ago
Discovery and Maintenance of Functional Dependencies by Independencies
For semantic query optimization one needs detailed knowledgeabout the contents of the database. Traditional techniquesuse static knowledgeabout all possible states of the database...
Siegfried Bell
153
Voted
ML
2006
ACM
110views Machine Learning» more  ML 2006»
15 years 5 months ago
Classification-based objective functions
Backpropagation, similar to most learning algorithms that can form complex decision surfaces, is prone to overfitting. This work presents classification-based objective functions, ...
Michael Rimer, Tony Martinez