Rights statement: © ACM, 2022. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference, July 2022 http://doi.acm.org/10.1145/3512290.3528789
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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Using phylogenetic analysis to enhance genetic improvement
AU - Rainford, Penelope
AU - Porter, Barry
N1 - © ACM, 2022. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference, July 2022 http://doi.acm.org/10.1145/3512290.3528789
PY - 2022/7/8
Y1 - 2022/7/8
N2 - Genetic code improvement systems (GI) start from an existing piece of program code and search for alternative versions with better performance according to a metric of interest. The search space of source code is a large, rough fitness landscape which can be extremely difficult to navigate. Most approaches to enhancing search capability in this domain involve either novelty search, where low-fitness areas are remembered and avoided, or formal analysis which attempts to find high-utility parameterizations for the GI process. In this paper we propose the use of phylogenetic analysis over genetic history to understand how different mutations and crossovers affect the fitness of a population over time for a particular problem; we use the results of that analysis to tune a GI process during its operation to enhance its ability to locate better program candidates. Using phylogenetic analysis on 600 runs of a genetic improver targeting a hash function, we demonstrate how the results of this analysis yield tuned mutation types over the course of a GI process (dynamically and continually set according to individual's ancestors' ranks within the population) to give hash functions with over 20% improved fitness compared to a baseline GI process.
AB - Genetic code improvement systems (GI) start from an existing piece of program code and search for alternative versions with better performance according to a metric of interest. The search space of source code is a large, rough fitness landscape which can be extremely difficult to navigate. Most approaches to enhancing search capability in this domain involve either novelty search, where low-fitness areas are remembered and avoided, or formal analysis which attempts to find high-utility parameterizations for the GI process. In this paper we propose the use of phylogenetic analysis over genetic history to understand how different mutations and crossovers affect the fitness of a population over time for a particular problem; we use the results of that analysis to tune a GI process during its operation to enhance its ability to locate better program candidates. Using phylogenetic analysis on 600 runs of a genetic improver targeting a hash function, we demonstrate how the results of this analysis yield tuned mutation types over the course of a GI process (dynamically and continually set according to individual's ancestors' ranks within the population) to give hash functions with over 20% improved fitness compared to a baseline GI process.
U2 - 10.1145/3512290.3528789
DO - 10.1145/3512290.3528789
M3 - Conference contribution/Paper
SP - 849
EP - 957
BT - GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference
A2 - Fieldsend, Jonathan E.
PB - ACM
CY - New York
T2 - Genetic and Evolutionary Computation Conference , GECCO 2022
Y2 - 9 July 2022 through 13 July 2022
ER -