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» On Genetic Algorithms and Lindenmayer Systems
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GECCO
2005
Springer
111views Optimization» more  GECCO 2005»
15 years 7 months ago
XCS with eligibility traces
The development of the XCS Learning Classifier System has produced a robust and stable implementation that performs competitively in direct-reward environments. Although investig...
Jan Drugowitsch, Alwyn Barry
GECCO
2005
Springer
158views Optimization» more  GECCO 2005»
15 years 7 months ago
Applying both positive and negative selection to supervised learning for anomaly detection
This paper presents a novel approach of applying both positive selection and negative selection to supervised learning for anomaly detection. It first learns the patterns of the n...
Xiaoshu Hang, Honghua Dai
143
Voted
ASC
2004
15 years 1 months ago
Soft computing applications in dynamic model identification of polymer extrusion process
This paper proposes the applications of soft computing to deal with the constraints in conventional modelling techniques of the dynamic extrusion process. The proposed technique i...
Leong Ping Tan, Ahmad Lotfi, Eugene Lai, J. B. Hul...
130
Voted
IVC
2002
148views more  IVC 2002»
15 years 1 months ago
Detecting lameness using 'Re-sampling Condensation' and 'multi-stream cyclic hidden Markov models'
A system for the tracking and classification of livestock movements is presented. The combined `tracker-classifier' scheme is based on a variant of Isard and Blakes `Condensa...
Derek R. Magee, Roger D. Boyle
115
Voted
ICSEA
2007
IEEE
15 years 8 months ago
Test Data Generation from UML State Machine Diagrams using GAs
Automatic test data generation helps testers to validate software against user requirements more easily. Test data can be generated from many sources; for example, experience of t...
Chartchai Doungsa-ard, Keshav P. Dahal, M. Alamgir...