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» Evolutionary testing of classes
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113
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CEC
2003
IEEE
15 years 8 months ago
Comparing neural networks and Kriging for fitness approximation in evolutionary optimization
Neural networks and the Kriging method are compared for constructing £tness approximation models in evolutionary optimization algorithms. The two models are applied in an identica...
Lars Willmes, Thomas Bäck, Yaochu Jin, Bernha...
109
Voted
CPHYSICS
2006
135views more  CPHYSICS 2006»
15 years 2 months ago
USPEX - Evolutionary crystal structure prediction
We approach the problem of computational crystal structure prediction, implementing an evolutionary algorithm--USPEX (Universal Structure Predictor: Evolutionary Xtallography). St...
Colin W. Glass, Artem R. Oganov, Nikolaus Hansen
106
Voted
IAT
2008
IEEE
15 years 9 months ago
Competency-Based Intelligent Curriculum Sequencing: Comparing Two Evolutionary Approaches
The process of creating e-learning contents using reusable learning objects (LOs) can be broken down in two sub-processes: LOs finding and LO sequencing. Although semiautomatic to...
Luis de Marcos, Roberto Barchino, José-Javi...
ECAL
2007
Springer
15 years 8 months ago
New Models for Old Questions: Evolutionary Robotics and the 'A Not B' Error
Abstract. In psychology the ‘A not B’ error, whereby infants perseverate in reaching to the location where a toy was previously hidden after it has been moved to a new location...
Rachel Wood, Ezequiel A. Di Paolo
114
Voted
SAC
2004
ACM
15 years 8 months ago
A comparison of randomized and evolutionary approaches for optimizing base station site selection
It is increasingly important to optimally select base stations in the design of cellular networks, as customers demand cheaper and better wireless services. From a set of potentia...
Larry Raisanen, Roger M. Whitaker, Steve Hurley