- author = "Edward Pantridge and Thomas Helmuth and Lee Spector",
- title = "Functional Code Building Genetic Programming",
- booktitle = "Proceedings of the 2022 Genetic and Evolutionary Computation Conference",
- year = "2022",
- editor = "Alma Rahat and Jonathan Fieldsend and Markus Wagner and Sara Tari and Nelishia Pillay and Irene Moser and Aldeida Aleti and Ales Zamuda and Ahmed Kheiri and Erik Hemberg and Christopher Cleghorn and Chao-li Sun and Georgios Yannakakis and Nicolas Bredeche and Gabriela Ochoa and Bilel Derbel and Gisele L. Pappa and Sebastian Risi and Laetitia Jourdan and Hiroyuki Sato and Petr Posik and Ofer Shir and Renato Tinos and John Woodward and Malcolm Heywood and Elizabeth Wanner and Leonardo Trujillo and Domagoj Jakobovic and Risto Miikkulainen and Bing Xue and Aneta Neumann and Richard Allmendinger and Inmaculada Medina-Bulo and Slim Bechikh and Andrew M. Sutton and Pietro Simone Oliveto",
- pages = "1000--1008",
- address = "Boston, USA",
- series = "GECCO '22",
- month = "9-13 " # jul,
- organisation = "SIGEVO",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, inductive program synthesis, automatic programming, linear genome, generational genetic algorithm, Lexicase Selection, PSB1",
- isbn13 = "978-1-4503-9237-2",
-
URL = "
https://arxiv.org/abs/2206.04561",
-
DOI = "
doi:10.1145/3512290.3528866",
-
video_url = "https://vimeo.com/723973432",
-
code_url = "https://github.com/erp12/cbgp-lite",
- size = "9 pages",
- abstract = "General program synthesis has become an important application area for genetic programming (GP), and for artificial intelligence more generally. Code Building Genetic Programming (CBGP) is a recently introduced GP method for general program synthesis that leverages reflection and first class specifications to support the evolution of programs that may use arbitrary data types, polymorphism, and functions drawn from existing codebases. However, neither a formal description nor a thorough benchmarking of CBGP have yet been reported. In this work, we formalize the method of CBGP using algorithms from type theory. Specially, we show that a functional programming language and a Hindley-Milner type system can be used to evolve type-safe programs using the process abstractly described in the original CBGP paper. Furthermore, we perform a comprehensive analysis of the search performance of this functional variant of CBGP compared to other contemporary GP program synthesis methods.",
-
notes = "p1000 CBGP 'produces code in a conventional
programming language, which can be read and understood
by humans'
p1001 'stack-based ... variable length, linear [plushy], genomes into type-safe abstract syntax trees (AST)'. Clojure.
p1002 'prove the type safety of the AST'. negative-to-zero benchmark problem. 'APP, ABS, and LET' 'open and close genes'
p1003 two stacks: AST, push chunks.
p1004 'simplification acts as a form of regularisation which improves the program generalizability to unseen data cases'
p1005 UMAD = Uniform Mutation by Addition and Deletion
p1007 'The exceptionally high generalization rate of CBGP is not easily explained.'
GECCO-2022 A Recombination of the 31st International Conference on Genetic Algorithms (ICGA) and the 27th Annual Genetic Programming Conference (GP)",
