- author = "Dario Izzo and Francesco Biscani and Alessio Mereta",
- title = "Differentiable Genetic Programming",
- booktitle = "EuroGP 2017: Proceedings of the 20th European Conference on Genetic Programming",
- year = "2017",
- month = "19-21 " # apr,
- editor = "Mauro Castelli and James McDermott and Lukas Sekanina",
- series = "LNCS",
- volume = "10196",
- publisher = "Springer Verlag",
- address = "Amsterdam",
- pages = "35--51",
- organisation = "species",
- note = "Nominated for best paper",
- keywords = "genetic algorithms, genetic programming, truncated Taylor polynomials, machine learning, symbolic regression, back-propagation",
- isbn13 = "978-3-319-55695-6",
-
URL = "
https://arxiv.org/abs/1611.04766",
-
DOI = "
10.1007/978-3-319-55696-3_3",
-
code_url = "https://darioizzo.github.io/dcgp/",
- abstract = "We introduce the use of high order automatic differentiation, implemented via the algebra of truncated Taylor polynomials, in genetic programming. Using the Cartesian Genetic Programming encoding we obtain a high-order Taylor representation of the program output that is then used to back-propagate errors during learning. The resulting machine learning framework is called differentiable Cartesian Genetic Programming (dCGP). In the context of symbolic regression, dCGP offers a new approach to the long unsolved problem of constant representation in GP expressions. On several problems of increasing complexity we find that dCGP is able to find the exact form of the symbolic expression as well as the constants values. We also demonstrate the use of dCGP to solve a large class of differential equations and to find prime integrals of dynamical systems, presenting, in both cases, results that confirm the efficacy of our approach.",
-
notes = "see also https://arxiv.org/abs/1611.04766
Part of \cite{Castelli:2017:GP} EuroGP'2017 held inconjunction with EvoCOP2017, EvoMusArt2017 and EvoApplications2017",
