• Suche: Genetic programming meets model-driven development

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Genetic Programming meets Model-Driven Development
Weise, T. ; Zapf, M. ; Ullah, M. ; et al.
7th International Conference on Hybrid Intelligent Systems (HIS 2007) Hybrid Intelligent Systems, 2007. HIS 2007. 7th International Conference on. :332-335 Sep, 2007

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2

Genetic Programming meets Model-Driven Development
Weise, Thomas ; Zapf, Michael ; Khan, Mohammad Ullah ; et al.

Genetische Programmierun... Evolutionäre Algorithmen... Genetic Programming Evolutionary Algorithms GP EA …
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3

Sustainable optimisation approaches for production planning and control to evolve towards industry 5.0.
Guerrero, Blanca ; Mula, Josefa ; Poler, Raul
International Journal of Production Research. Nov2025, Vol. 63 Issue 21, p8091-8123. 33p.

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5

Genetic Programming meets Model-Driven Development
Thomas Weise ; Michael Zapf ; Mohammad Ullah Khan ; et al.
http://www.it-weise.de/documents/files/WZKG2007DGPFg.pdf.

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9

The Premature Obituary of Programming: Why deep learning will not replace programming.
Yellin, Daniel M.
Communications of the ACM. Feb2023, Vol. 66 Issue 2, p41-44. 4p. 1 Color Photograph.

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10

Human–machine collaborative rehabilitation toward Industry 5.0: principles, technologies and challenges.
Pan, Peiyan ; Hu, Bingtao ; Feng, Yixiong ; et al.
International Journal of Production Research. Nov2025, p1-48. 48p. 4 Illustrations.

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11

Develop a Hybrid Intelligent Fuzzy Self-Adaptive Mutated Genetic Algorithm and Deep Reinforcement Learning for Efficient Home Energy Management.
Vaithiyanathan, Balamurugan ; Jothi, Kavitha Ramaswami ; Paramasivam, Suresh Kumar
Traitement du Signal. Apr2025, Vol. 42 Issue 2, p1131-1148. 18p.

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12

Determining optimal batch interval and production strategy based on available-to-promise model for order promising.
Liao, Lu-Wen ; Tsao, Yu-Chung ; Hung, Yu-Han
Annals of Operations Research. Jun2025, Vol. 349 Issue 1, p315-338. 24p.

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13

Individual differences in computer programming: a systematic review.
Navarro-Cota, Christian ; Molina, Ana I. ; Redondo, Miguel A. ; et al.
Behaviour & Information Technology. Feb2025, Vol. 44 Issue 2, p357-375. 19p.

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14

Artificial intelligent technologies in Japanese manufacturing firms: an empirical survey study.
Hadid, Wael ; Horii, Satoshi ; Yokota, Akinori
International Journal of Production Research. Jan2025, Vol. 63 Issue 1, p193-219. 27p.

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15

A factorisation-based recommendation model for customised products configuration design.
Zhou, Huifang ; Zhang, Shuyou ; Qiu, Lemiao ; et al.
International Journal of Production Research. Oct2023, Vol. 61 Issue 19, p6381-6402. 22p. 2 Charts, 7 Graphs.

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16

A multiobjective single bus corridor scheduling using machine learning-based predictive models.
Chen, Bing ; Bai, Ruibin ; Li, Jiawei ; et al.
International Journal of Production Research. Jan 2023, Vol. 61 Issue 1, p131-145. 15p. 1 Diagram, 1 Chart, 6 Graphs.

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17

Automated Runtime Verification of Security for E-Commerce Smart Contracts.
Liu, Yang ; Zhang, Shengjie ; Ma, Yan
Journal of Theoretical & Applied Electronic Commerce Research. Jun2025, Vol. 20 Issue 2, p73. 25p.

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18

A sensor-driven operations and maintenance planning approach for large-scale leased manufacturing systems.
Karakaya, Şakir ; Yildirim, Murat ; Gebraeel, Nagi ; et al.
International Journal of Production Research. Dec2024, Vol. 62 Issue 24, p8701-8718. 18p.

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19

Towards AI driven surface roughness evaluation in manufacturing: a prospective study.
Ghosh, Sourish ; Knoblauch, Ricardo ; El Mansori, Mohamed ; et al.
Journal of Intelligent Manufacturing. Oct2025, Vol. 36 Issue 7, p4519-4548. 30p.

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20

Rescheduling Under Disruption: A Product-Driven Framework with Heuristic and Reinforcement Learning Strategies.
SÁEZ, Patricio ; RIVERA, JAIME ; SALAS, PATRICIO ; et al.
Management & Production Engineering Review (MPER). Sep2025, Vol. 16 Issue 3, p1-15. 15p.

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