Facilitating evolutionary algorithm analysis with persistent data structures

Publikation: Beitrag in Buch/Bericht/TagungsbandKonferenzbeitragBegutachtung

3 Zitate (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.

OriginalspracheEnglisch
TitelComputer Aided Systems Theory – EUROCAST 2017 - 16th International Conference, Revised Selected Papers
Redakteure/-innenRoberto Moreno-Diaz, Alexis Quesada-Arencibia, Franz Pichler
Herausgeber (Verlag)Springer
Seiten416-423
Seitenumfang8
ISBN (Print)9783319747170
DOIs
PublikationsstatusVeröffentlicht - 2018
Veranstaltung16th International Conference on Computer Aided Systems Theory, EUROCAST 2017 - Las Palmas de Gran Canaria, Spanien
Dauer: 19 Feb. 201724 Feb. 2017

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band10671 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz16th International Conference on Computer Aided Systems Theory, EUROCAST 2017
Land/GebietSpanien
OrtLas Palmas de Gran Canaria
Zeitraum19.02.201724.02.2017

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