- author = "Alexander Wild and Barry Porter",
- title = "Neurally Guided Transfer Learning for Genetic Programming",
- 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 = "267--268",
- 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, Transfer Learning, Neural Networks, ANN: Poster",
- isbn13 = "978-1-4503-8351-6",
-
DOI = "
doi:10.1145/3449726.3459511",
- size = "2 pages",
- abstract = "A key challenge in GP is how to learn from the past, so that the successful synthesis of simple programs can feed in to more challenging unsolved programs. we present a transfer learning (TL) mechanism for GP which extracts fragments from programs it synthesises, then employs deep neural networks to identify new problems to deploy them into, to boost performance. This end-to-end system requires no human identification of which programs to use as donors for TL, the system only needs IO examples.",
-
notes = "
GECCO-2021 A Recombination of the 30th International Conference on Genetic Algorithms (ICGA) and the 26th Annual Genetic Programming Conference (GP)",
