- author = "Christian Raymond and Qi Chen and Bing Xue and Mengjie Zhang",
- title = "Multi-objective Genetic Programming for Symbolic Regression with the Adaptive Weighted Splines Representation",
- booktitle = "Proceedings of the 2021 Genetic and Evolutionary Computation Conference Companion",
- year = "2021",
- editor = "Francisco Chicano and Alberto Tonda and Krzysztof Krawiec and Marde Helbig and Christopher W. Cleghorn and Dennis G. Wilson and Georgios Yannakakis and Luis Paquete and Gabriela Ochoa and Jaume Bacardit and Christian Gagne and Sanaz Mostaghim and Laetitia Jourdan and Oliver Schuetze and Petr Posik and Carlos Segura and Renato Tinos and Carlos Cotta and Malcolm Heywood and Mengjie Zhang and Leonardo Trujillo and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Fuyuki Ishikawa and Inmaculada Medina-Bulo and Frank Neumann and Andrew M. Sutton",
- pages = "165--166",
- address = "internet",
- series = "GECCO '21",
- month = jul # " 10-14",
- organisation = "SIGEVO",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, Symbolic Regression, Generalization, Model Complexity, Evolutionary Multi-objective Optimization: Poster",
- isbn13 = "978-1-4503-8351-6",
-
DOI = "
doi:10.1145/3449726.3459461",
- size = "2 pages",
- abstract = "Genetic Programming (GP) for symbolic regression often generates over-complex models, which overfit the training data and have poor generalisation onto unseen data. One recent work investigated controlling model complexity by using a new GP representation called Adaptive Weighted Splines (AWS), which is a semi-structured representation that can control the model complexity explicitly. This work extends this previous work by incorporating a new parsimony pressure objective to further control the model complexity. Experimental results demonstrate that the new multi-objective GP method consistently obtains superior fronts and produces better generalising models compared to single-objective GP with both the tree-based and AWS representation as well a multi-objective tree-based GP method with parsimony pressure.",
-
notes = "
GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)",
