- author = "Thomas Helmuth and Edward Pantridge and Grace Woolson and Lee Spector",
- title = "Genetic Source Sensitivity and Transfer Learning in Genetic Programming",
- booktitle = "2020 Conference on Artificial Life",
- year = "2020",
- editor = "Josh Bongard and Juniper Lovato and Laurent Hebert-Dufresne and Radhakrishna Dasari and Lisa Soros",
- pages = "303--311",
- address = "online",
- month = "13-18 " # jul,
- organisation = "ISAL",
- publisher = "Massachusetts Institute of Technology",
- keywords = "genetic algorithms, genetic programming, Push",
-
URL = "
https://direct.mit.edu/isal/proceedings/isal2020/32/1/98387",
-
URL = "
https://direct.mit.edu/isal/proceedings-pdf/isal2020/32/303/1908486/isal_a_00326.pdf",
-
DOI = "
doi:10.1162/isal_a_00326",
- 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.",
-
notes = "Program Synthesis Benchmark Problems
Montreal, Canada,
isal_a_00357.pdf",
