Mining Patterns from Genetic Improvement Experiments

Oliver Krauss; Hanspeter Mössenböck; Michael Affenzeller

doi:10.1109/GI.2019.00015

Mining Patterns from Genetic Improvement Experiments

Oliver Krauss, Hanspeter Mössenböck, Michael Affenzeller

Research Center Hagenberg

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-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 language	English
Title of host publication	Proceedings - 2019 IEEE/ACM 6th International Workshop on Genetic Improvement, GI 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	28-29
Number of pages	2
ISBN (Electronic)	9781728122687
DOIs	https://doi.org/10.1109/GI.2019.00015
Publication status	Published - May 2019
Event	6th IEEE/ACM International Workshop on Genetic Improvement, GI 2019 - Montreal, Canada Duration: 28 May 2019 → …

Publication series

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

Conference

Conference	6th IEEE/ACM International Workshop on Genetic Improvement, GI 2019
Country/Territory	Canada
City	Montreal
Period	28.05.2019 → …

Keywords

Abstract Syntax Tree
Frequent Subgraph Mining
Genetic Improvement
Pattern Mining

Access to Document

10.1109/GI.2019.00015

Cite this

Krauss, O., Mössenböck, H., & Affenzeller, M. (2019). Mining Patterns from Genetic Improvement Experiments. In Proceedings - 2019 IEEE/ACM 6th International Workshop on Genetic Improvement, GI 2019 (pp. 28-29). Article 8823638 (Proceedings - 2019 IEEE/ACM 6th International Workshop on Genetic Improvement, GI 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GI.2019.00015

Krauss, Oliver ; Mössenböck, Hanspeter ; Affenzeller, Michael. / Mining Patterns from Genetic Improvement Experiments. Proceedings - 2019 IEEE/ACM 6th International Workshop on Genetic Improvement, GI 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 28-29 (Proceedings - 2019 IEEE/ACM 6th International Workshop on Genetic Improvement, GI 2019).

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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.",

keywords = "Abstract Syntax Tree, Frequent Subgraph Mining, Genetic Improvement, Pattern Mining",

author = "Oliver Krauss and Hanspeter M{\"o}ssenb{\"o}ck and Michael Affenzeller",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 6th IEEE/ACM International Workshop on Genetic Improvement, GI 2019 ; Conference date: 28-05-2019",

year = "2019",

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doi = "10.1109/GI.2019.00015",

language = "English",

series = "Proceedings - 2019 IEEE/ACM 6th International Workshop on Genetic Improvement, GI 2019",

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Krauss, O, Mössenböck, H & Affenzeller, M 2019, Mining Patterns from Genetic Improvement Experiments. in Proceedings - 2019 IEEE/ACM 6th International Workshop on Genetic Improvement, GI 2019., 8823638, Proceedings - 2019 IEEE/ACM 6th International Workshop on Genetic Improvement, GI 2019, Institute of Electrical and Electronics Engineers Inc., pp. 28-29, 6th IEEE/ACM International Workshop on Genetic Improvement, GI 2019, Montreal, Canada, 28.05.2019. https://doi.org/10.1109/GI.2019.00015

Mining Patterns from Genetic Improvement Experiments. / Krauss, Oliver; Mössenböck, Hanspeter; Affenzeller, Michael.
Proceedings - 2019 IEEE/ACM 6th International Workshop on Genetic Improvement, GI 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 28-29 8823638 (Proceedings - 2019 IEEE/ACM 6th International Workshop on Genetic Improvement, GI 2019).

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

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AB - 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.

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KW - Pattern Mining

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

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Krauss O, Mössenböck H, Affenzeller M. Mining Patterns from Genetic Improvement Experiments. In Proceedings - 2019 IEEE/ACM 6th International Workshop on Genetic Improvement, GI 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 28-29. 8823638. (Proceedings - 2019 IEEE/ACM 6th International Workshop on Genetic Improvement, GI 2019). doi: 10.1109/GI.2019.00015

Mining Patterns from Genetic Improvement Experiments

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