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Towards a Pittsburgh-style LCS for learning manufacturing m..:
, In:
Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
,
Heider, Michael
;
Pätzel, David
;
Hähner, Jörg
- p. 127-128 , 2020
Link:
https://dl.acm.org/doi/10.1145/3377929.3389963
RT T1 <cTitle>Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion</cTitle>: T1 <Title>Towards a Pittsburgh-style LCS for learning manufacturing machinery parametrizations</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3389963&Exemplar=1&LAN=DE A1 Heider, Michael A1 Pätzel, David A1 Hähner, Jörg PB ACM YR 2020 K1 configuration K1 evolutionary machine learning K1 learning classifier systems K1 manufacturing K1 rule-based learning K1 Computing methodologies K1 Machine learning K1 Learning paradigms K1 Supervised learning K1 Supervised learning by regression K1 Machine learning approaches K1 Rule learning SP 127 OP 128 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3377929.3389963 DO https://dl.acm.org/doi/10.1145/3377929.3389963 SF ELIB - SuUB Bremen
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