Solving Novel Program Synthesis Problems with Genetic Programming Using Parametric Polymorphism
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{pantridge:2023:GECCO,
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author = "Edward Pantridge and Thomas Helmuth",
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title = "Solving Novel Program Synthesis Problems with Genetic
Programming Using Parametric Polymorphism",
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booktitle = "Proceedings of the 2023 Genetic and Evolutionary
Computation Conference",
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year = "2023",
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editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and
Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and
Arnaud Liefooghe and Bing Xue and Ying Bi and
Nelishia Pillay and Irene Moser and Arthur Guijt and
Jessica Catarino and Pablo Garcia-Sanchez and
Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
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pages = "1175--1183",
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address = "Lisbon, Portugal",
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series = "GECCO '23",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, inductive
program synthesis, automatic programming,
polymorphism",
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isbn13 = "9798400701191",
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DOI = "doi:10.1145/3583131.3590502",
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size = "9 pages",
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abstract = "Contemporary genetic programming (GP) systems for
general program synthesis have been primarily concerned
with evolving programs that can manipulate values from
a standard set of primitive data types and simple
indexed data structures. In contrast, human programmers
do not limit themselves to a small finite set of data
types and use polymorphism to express an unbounded
number of types including nested data structures,
product types, and generic functions. Code-building
Genetic Programming (CBGP) is a recently introduced
method that compiles type-safe programs from linear
genomes using stack-based compilation and a formal type
system. Although prior work with CBGP has shown initial
demonstrations of polymorphism inside evolved programs,
we have provided a deeper exploration of these
capabilities through the evolution of programs which
make use of generic data types such as key-value maps,
tuples, and sets, as well as higher order functions and
functions with polymorphic type signatures. In our
experiments, CBGP is able to solve problems with all of
these properties, where every other GP system that we
know of has restrictions that make it unable to even
consider problems with these properties. This
demonstration provides a significant step towards fully
aligning the expressiveness of GP to real world
programming.",
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notes = "GECCO-2023 A Recombination of the 32nd International
Conference on Genetic Algorithms (ICGA) and the 28th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Edward R Pantridge
Thomas Helmuth
Citations