Genetic Source Sensitivity and Transfer Learning in Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Helmuth:2020:ALife,
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author = "Thomas Helmuth and Edward Pantridge and
Grace Woolson and Lee Spector",
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title = "Genetic Source Sensitivity and Transfer Learning in
Genetic Programming",
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booktitle = "2020 Conference on Artificial Life",
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year = "2020",
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editor = "Josh Bongard and Juniper Lovato and
Laurent Hebert-Dufresne and Radhakrishna Dasari and
Lisa Soros",
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pages = "303--311",
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address = "online",
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month = "13-18 " # jul,
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organisation = "ISAL",
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publisher = "Massachusetts Institute of Technology",
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keywords = "genetic algorithms, genetic programming, Push",
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URL = "https://direct.mit.edu/isal/proceedings/isal2020/32/1/98387",
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URL = "https://direct.mit.edu/isal/proceedings-pdf/isal2020/32/303/1908486/isal_a_00326.pdf",
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DOI = "doi:10.1162/isal_a_00326",
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abstract = "Genetic programming uses biologically-inspired
processes of variation and selection to synthesize
computer programs that solve problems. Here we
investigate the sensitivity of genetic programming to
changes in the probability that particular instructions
and constants will be chosen for inclusion in randomly
generated programs or for introduction by mutation. We
find, contrary to conventional wisdom within the field,
that genetic programming can be highly sensitive to
changes in this source of new genetic material.
Additionally, we find that genetic sources can be tuned
to significantly improve adaptation across sets of
related problems. We study the evolution of solutions
to software synthesis problems using untuned genetic
sources and sources that have been tuned on the basis
of problem statements, human intuition, or prevalence
in prior solution programs. We find significant
differences in performance across these approaches, and
use these lessons to develop a method for tuning
genetic sources on the basis of evolved solutions to
related problems. This transfer learning approach tunes
genetic sources nearly as well as humans do, but by
means of a fully automated process that can be applied
to previously unsolved problems.",
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notes = "Program Synthesis Benchmark Problems
Montreal, Canada,
isal_a_00357.pdf",
- }
Genetic Programming entries for
Thomas Helmuth
Edward R Pantridge
Grace Woolson
Lee Spector
Citations