Mining Patterns from Genetic Improvement Experiments

Oliver Krauss, Hanspeter Mössenböck, Michael Affenzeller

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

1 Citation (Scopus)

Abstract

When conducting genetic improvement experiments, a large amount of individuals (≈ population size ∗ generations) is created and evaluated. The corresponding experiments contain valuable data concerning the fitness of individuals for the defined criteria, such as run-time performance, memory use or robustness. This publication presents an approach to utilize this information in order to identify recurring context independent patterns in abstract syntax trees (ASTs). These patterns can be applied for restricting the search space (in the form of anti-patterns) or for grafting operators in the population. Future work includes an evaluation of this approach, as well as extending it with wildcards and class hierarchies for larger and more generalized patterns.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE/ACM 6th International Workshop on Genetic Improvement, GI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages28-29
Number of pages2
ISBN (Electronic)9781728122687
DOIs
Publication statusPublished - May 2019
Event6th IEEE/ACM International Workshop on Genetic Improvement, GI 2019 - Montreal, Canada
Duration: 28 May 2019 → …

Publication series

NameProceedings - 2019 IEEE/ACM 6th International Workshop on Genetic Improvement, GI 2019

Conference

Conference6th IEEE/ACM International Workshop on Genetic Improvement, GI 2019
CountryCanada
CityMontreal
Period28.05.2019 → …

Keywords

  • Abstract Syntax Tree
  • Frequent Subgraph Mining
  • Genetic Improvement
  • Pattern Mining

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