- author = "Christian Raymond and Qi Chen and Bing Xue and Mengjie Zhang",
- title = "Genetic Programming with Rademacher Complexity for Symbolic Regression",
- booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019",
- year = "2019",
- editor = "Carlos A. Coello Coello",
- pages = "2657--2664",
- address = "Wellington, New Zealand",
- month = "10-13 " # jun,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, SVM, symbolic regression, generalisation, structural risk minimisation, Rademacher complexity: Poster",
- isbn13 = "978-1-7281-2152-6",
-
URL = "
https://github.com/Decadz/Genetic-Programming-with-Rademacher-Complexity",
-
DOI = "
doi:10.1109/CEC.2019.8790341",
- size = "8 pages",
- abstract = "Genetic Programming (GP) for symbolic regression is often prone to overfitting the training data, causing poor performance on unseen data. A number of recent works in the field have been devoted to regulating this problem by investigating both the structural and functional complexity of GP individuals during the evolutionary process. This work uses the Rademacher complexity and incorporates it into the fitness function of GP, using it as a means of controlling the functional complexity of GP individuals. The experiment results confirm that the new GP method has a notable generalization gain compared to the standard GP and Support Vector Regression (SVR) in most of the considered problems. Further investigations also show that the new GP method generates symbolic regression models that could not only release the over-fitting trend in standard GP but also are significantly smaller in size compared to their counterparts in standard GP.",
-
notes = "also known as \cite{8790341}
IEEE Catalog Number: CFP19ICE-ART",
