- author = "Christian Raymond and Qi Chen and Bing Xue and Mengjie Zhang",
- title = "Adaptive Weighted Splines: A New Representation to Genetic Programming for Symbolic Regression",
- year = "2020",
- editor = "Carlos Artemio {Coello Coello} and Arturo Hernandez Aguirre and Josu Ceberio Uribe and Mario Garza Fabre and Gregorio {Toscano Pulido} and Katya Rodriguez-Vazquez and Elizabeth Wanner and Nadarajen Veerapen and Efren Mezura Montes and Richard Allmendinger and Hugo Terashima Marin and Markus Wagner and Thomas Bartz-Beielstein and Bogdan Filipic and Heike Trautmann and Ke Tang and John Koza and Erik Goodman and William B. Langdon and Miguel Nicolau and Christine Zarges and Vanessa Volz and Tea Tusar and Boris Naujoks and Peter A. N. Bosman and Darrell Whitley and Christine Solnon and Marde Helbig and Stephane Doncieux and Dennis G. Wilson and Francisco {Fernandez de Vega} and Luis Paquete and Francisco Chicano and Bing Xue and Jaume Bacardit and Sanaz Mostaghim and Jonathan Fieldsend and Oliver Schuetze and Dirk Arnold and Gabriela Ochoa and Carlos Segura and Carlos Cotta and Michael Emmerich and Mengjie Zhang and Robin Purshouse and Tapabrata Ray and Justyna Petke and Fuyuki Ishikawa and Johannes Lengler and Frank Neumann",
- isbn13 = "9781450371285",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
-
URL = "
https://doi.org/10.1145/3377930.3390244",
-
DOI = "
doi:10.1145/3377930.3390244",
- booktitle = "Proceedings of the 2020 Genetic and Evolutionary Computation Conference",
- pages = "1003--1011",
- size = "9 pages",
- keywords = "genetic algorithms, genetic programming, symbolic regression, representation, generalization, spline",
- address = "internet",
- series = "GECCO '20",
- month = jul # " 8-12",
- organisation = "SIGEVO",
- abstract = "Genetic Programming for Symbolic Regression is often prone to overfit the training data, resulting in poor generalization on unseen data. To address this issue, many pieces of research have been devoted to regularization via controlling the model complexity. However, due to the unstructured tree based representation of individuals the model complexity cannot be directly computed, rather approximation of the complexity must be taken. This paper proposes a new novel representation called Adaptive Weighted Splines which enables explicit control over the complexity of individuals using splines. The experimental results confirm that this new representation is significantly better than the tree-based representation at avoiding overfitting and generalizing on unseen data, demonstrating notably better and far more consistent generalization performances on all the benchmark problems. Further analysis also shows that in most cases, the new Genetic Programming method outperforms classical regression techniques such as Linear Regression, Support Vector Regression, K-Nearest Neighbour and Decision Tree Regression and performs competitively with state-of-the-art ensemble regression methods Random Forests and Gradient Boosting.",
-
notes = "Ordered vector of basic spline knots, Bspline.
AWS
Also known as \cite{10.1145/3377930.3390244} GECCO-2020 A Recombination of the 29th International Conference on Genetic Algorithms (ICGA) and the 25th Annual Genetic Programming Conference (GP)",
