Comparing optimistic and pessimistic constraint evaluation in shape-constrained symbolic regression

Author:

Haider Christian1,de França Fabrício Olivetti2,Kronberger Gabriel1,Burlacu Bogdan1

Affiliation:

1. University of Applied Sciences Upper Austria, Hagenberg, Austria

2. Universidade Federal do ABC, Brazil

Funder

Christian Doppler Forschungsgesellschaft

Publisher

ACM

Reference24 articles.

1. Pierre-Cyril Aubin-Frankowski and Zoltan Szabo . 2020 . Hard Shape-Constrained Kernel Machines. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H . Lin (Eds.) , Vol. 33 . Curran Associates, Inc., 384--395. Pierre-Cyril Aubin-Frankowski and Zoltan Szabo. 2020. Hard Shape-Constrained Kernel Machines. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 384--395.

2. Nathan Baker , Frank Alexander , Timo Bremer , Aric Hagberg , Yannis Kevrekidis , Habib Najm , Manish Parashar , Abani Patra , James Sethian , Stefan Wild , 2019 . Workshop report on basic research needs for scientific machine learning: Core technologies for artificial intelligence. Technical Report. USDOE Office of Science (SC), Washington, DC (United States). Nathan Baker, Frank Alexander, Timo Bremer, Aric Hagberg, Yannis Kevrekidis, Habib Najm, Manish Parashar, Abani Patra, James Sethian, Stefan Wild, et al. 2019. Workshop report on basic research needs for scientific machine learning: Core technologies for artificial intelligence. Technical Report. USDOE Office of Science (SC), Washington, DC (United States).

3. Statistical inference under order restrictions: the theory and application of isotonic regression;Brunk H. D.;International Statistical Review,1972

4. Constraint-Handling Techniques used with Evolutionary Algorithms

5. A fast and elitist multiobjective genetic algorithm: NSGA-II

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