Facilitating evolutionary algorithm analysis with persistent data structures

Erik Pitzer; Michael Affenzeller

doi:10.1007/978-3-319-74718-7_50

Facilitating evolutionary algorithm analysis with persistent data structures

Erik Pitzer, Michael Affenzeller

Research Center Hagenberg

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

3 Citations (Scopus)

Abstract

Evolutionary algorithm analysis is often impeded by the large amounts of intermediate data that is usually discarded and has to be painstakingly reconstructed for real-world large-scale applications. In the recent past persistent data structures have been developed which offer extremely compact storage with acceptable runtime penalties. In this work two promising persistent data structures are explored in the context of evolutionary computation with the hope to open the door to simplified analysis of large-scale evolutionary algorithm runs.

Original language	English
Title of host publication	Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers
Editors	Roberto Moreno-Diaz, Alexis Quesada-Arencibia, Franz Pichler
Publisher	Springer
Pages	416-423
Number of pages	8
ISBN (Print)	9783319747170
DOIs	https://doi.org/10.1007/978-3-319-74718-7_50
Publication status	Published - 2018
Event	16th International Conference on Computer Aided Systems Theory, EUROCAST 2017 - Las Palmas de Gran Canaria, Spain Duration: 19 Feb 2017 → 24 Feb 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10671 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Computer Aided Systems Theory, EUROCAST 2017
Country/Territory	Spain
City	Las Palmas de Gran Canaria
Period	19.02.2017 → 24.02.2017

Access to Document

10.1007/978-3-319-74718-7_50

Cite this

Pitzer, E., & Affenzeller, M. (2018). Facilitating evolutionary algorithm analysis with persistent data structures. In R. Moreno-Diaz, A. Quesada-Arencibia, & F. Pichler (Eds.), Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers (pp. 416-423). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10671 LNCS). Springer. https://doi.org/10.1007/978-3-319-74718-7_50

Pitzer, Erik ; Affenzeller, Michael. / Facilitating evolutionary algorithm analysis with persistent data structures. Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers. editor / Roberto Moreno-Diaz ; Alexis Quesada-Arencibia ; Franz Pichler. Springer, 2018. pp. 416-423 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Facilitating evolutionary algorithm analysis with persistent data structures",

abstract = "Evolutionary algorithm analysis is often impeded by the large amounts of intermediate data that is usually discarded and has to be painstakingly reconstructed for real-world large-scale applications. In the recent past persistent data structures have been developed which offer extremely compact storage with acceptable runtime penalties. In this work two promising persistent data structures are explored in the context of evolutionary computation with the hope to open the door to simplified analysis of large-scale evolutionary algorithm runs.",

author = "Erik Pitzer and Michael Affenzeller",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2018. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.; 16th International Conference on Computer Aided Systems Theory, EUROCAST 2017 ; Conference date: 19-02-2017 Through 24-02-2017",

year = "2018",

doi = "10.1007/978-3-319-74718-7_50",

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booktitle = "Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers",

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Pitzer, E & Affenzeller, M 2018, Facilitating evolutionary algorithm analysis with persistent data structures. in R Moreno-Diaz, A Quesada-Arencibia & F Pichler (eds), Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10671 LNCS, Springer, pp. 416-423, 16th International Conference on Computer Aided Systems Theory, EUROCAST 2017, Las Palmas de Gran Canaria, Spain, 19.02.2017. https://doi.org/10.1007/978-3-319-74718-7_50

Facilitating evolutionary algorithm analysis with persistent data structures. / Pitzer, Erik ; Affenzeller, Michael.
Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers. ed. / Roberto Moreno-Diaz; Alexis Quesada-Arencibia; Franz Pichler. Springer, 2018. p. 416-423 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10671 LNCS).

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

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PY - 2018

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AB - Evolutionary algorithm analysis is often impeded by the large amounts of intermediate data that is usually discarded and has to be painstakingly reconstructed for real-world large-scale applications. In the recent past persistent data structures have been developed which offer extremely compact storage with acceptable runtime penalties. In this work two promising persistent data structures are explored in the context of evolutionary computation with the hope to open the door to simplified analysis of large-scale evolutionary algorithm runs.

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SN - 9783319747170

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 423

BT - Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers

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A2 - Quesada-Arencibia, Alexis

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Pitzer E , Affenzeller M. Facilitating evolutionary algorithm analysis with persistent data structures. In Moreno-Diaz R, Quesada-Arencibia A, Pichler F, editors, Computer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers. Springer. 2018. p. 416-423. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-74718-7_50

Facilitating evolutionary algorithm analysis with persistent data structures

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