ELEA – Build your own evolutionary algorithm in your browser

Markus Wagner; Erik Kohlros; Gerome Quantmeyer; Timo Kötzing

doi:10.1145/3583133.3590723

ELEA – Build your own evolutionary algorithm in your browser

Markus Wagner, Erik Kohlros, Gerome Quantmeyer, Timo Kötzing

Department of Data Science & AI

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

Abstract

We provide an open source framework to experiment with evolutionary algorithms which we call Experimenting and Learning toolkit for Evolutionary Algorithms (ELEA). ELEA is browser-based and allows to assemble evolutionary algorithms using drag-and-drop, starting from a number of simple pre-designed examples, making the startup costs for employing the toolkit minimal. The designed examples can be executed and collected data can be displayed graphically. Further features include export of algorithm designs and experimental results as well as multi-threading. With the very intuitive user interface and the short time to get initial experiments going, this tool is especially suitable for explorative analyses of algorithms as well as for the use in classrooms.

Original language	English
Title of host publication	Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion
Editors	Luís Paquete
Place of Publication	New York NY USA
Publisher	Association for Computing Machinery (ACM)
Pages	519-522
Number of pages	4
ISBN (Electronic)	9798400701207
DOIs	https://doi.org/10.1145/3583133.3590723
Publication status	Published - 2023
Event	The Genetic and Evolutionary Computation Conference 2023 - Lisbon, Portugal Duration: 15 Jul 2023 → 19 Jul 2023 https://dl.acm.org/doi/proceedings/10.1145/3583131 (proceedings) https://dl.acm.org/doi/proceedings/10.1145/3583133 (Companion) https://gecco-2023.sigevo.org/HomePage (Website)

Conference

Conference	The Genetic and Evolutionary Computation Conference 2023
Abbreviated title	GECCO 2023
Country/Territory	Portugal
City	Lisbon
Period	15/07/23 → 19/07/23
Internet address	https://dl.acm.org/doi/proceedings/10.1145/3583131 (proceedings) https://dl.acm.org/doi/proceedings/10.1145/3583133 (Companion) https://gecco-2023.sigevo.org/HomePage (Website)

Keywords

Benchmarking
education
tool

Access to Document

10.1145/3583133.3590723

Cite this

@inproceedings{214be87fcb504f47b15fb47a921a51f7,

title = "ELEA – Build your own evolutionary algorithm in your browser",

abstract = "We provide an open source framework to experiment with evolutionary algorithms which we call Experimenting and Learning toolkit for Evolutionary Algorithms (ELEA). ELEA is browser-based and allows to assemble evolutionary algorithms using drag-and-drop, starting from a number of simple pre-designed examples, making the startup costs for employing the toolkit minimal. The designed examples can be executed and collected data can be displayed graphically. Further features include export of algorithm designs and experimental results as well as multi-threading. With the very intuitive user interface and the short time to get initial experiments going, this tool is especially suitable for explorative analyses of algorithms as well as for the use in classrooms.",

keywords = "Benchmarking, education, tool",

author = "Markus Wagner and Erik Kohlros and Gerome Quantmeyer and Timo K{\"o}tzing",

note = "Funding Information: We thank Antony Kamp, Bjarne Sievers, and Oscar Manglaras for their contributions to early versions of ELEA. We thank Xiaoyue Li for her feedback on an earlier version. We thank the reviewers for their many suggestions for future directions. Publisher Copyright: {\textcopyright} 2023 Copyright held by the owner/author(s).; The Genetic and Evolutionary Computation Conference 2023, GECCO 2023 ; Conference date: 15-07-2023 Through 19-07-2023",

year = "2023",

doi = "10.1145/3583133.3590723",

language = "English",

pages = "519--522",

editor = "Lu{\'i}s Paquete",

booktitle = "Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion",

publisher = "Association for Computing Machinery (ACM)",

address = "United States of America",

url = "https://dl.acm.org/doi/proceedings/10.1145/3583131, https://dl.acm.org/doi/proceedings/10.1145/3583133, https://gecco-2023.sigevo.org/HomePage",

}

Wagner, M, Kohlros, E, Quantmeyer, G & Kötzing, T 2023, ELEA – Build your own evolutionary algorithm in your browser. in L Paquete (ed.), Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion. Association for Computing Machinery (ACM), New York NY USA, pp. 519-522, The Genetic and Evolutionary Computation Conference 2023, Lisbon, Portugal, 15/07/23. https://doi.org/10.1145/3583133.3590723

ELEA – Build your own evolutionary algorithm in your browser. / Wagner, Markus; Kohlros, Erik; Quantmeyer, Gerome et al.
Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion. ed. / Luís Paquete. New York NY USA: Association for Computing Machinery (ACM), 2023. p. 519-522.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - ELEA – Build your own evolutionary algorithm in your browser

AU - Wagner, Markus

AU - Kohlros, Erik

AU - Quantmeyer, Gerome

AU - Kötzing, Timo

N1 - Funding Information: We thank Antony Kamp, Bjarne Sievers, and Oscar Manglaras for their contributions to early versions of ELEA. We thank Xiaoyue Li for her feedback on an earlier version. We thank the reviewers for their many suggestions for future directions. Publisher Copyright: © 2023 Copyright held by the owner/author(s).

PY - 2023

Y1 - 2023

N2 - We provide an open source framework to experiment with evolutionary algorithms which we call Experimenting and Learning toolkit for Evolutionary Algorithms (ELEA). ELEA is browser-based and allows to assemble evolutionary algorithms using drag-and-drop, starting from a number of simple pre-designed examples, making the startup costs for employing the toolkit minimal. The designed examples can be executed and collected data can be displayed graphically. Further features include export of algorithm designs and experimental results as well as multi-threading. With the very intuitive user interface and the short time to get initial experiments going, this tool is especially suitable for explorative analyses of algorithms as well as for the use in classrooms.

AB - We provide an open source framework to experiment with evolutionary algorithms which we call Experimenting and Learning toolkit for Evolutionary Algorithms (ELEA). ELEA is browser-based and allows to assemble evolutionary algorithms using drag-and-drop, starting from a number of simple pre-designed examples, making the startup costs for employing the toolkit minimal. The designed examples can be executed and collected data can be displayed graphically. Further features include export of algorithm designs and experimental results as well as multi-threading. With the very intuitive user interface and the short time to get initial experiments going, this tool is especially suitable for explorative analyses of algorithms as well as for the use in classrooms.

KW - Benchmarking

KW - education

KW - tool

UR - http://www.scopus.com/inward/record.url?scp=85169026758&partnerID=8YFLogxK

U2 - 10.1145/3583133.3590723

DO - 10.1145/3583133.3590723

M3 - Conference Paper

AN - SCOPUS:85169026758

SP - 519

EP - 522

BT - Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion

A2 - Paquete, Luís

PB - Association for Computing Machinery (ACM)

CY - New York NY USA

T2 - The Genetic and Evolutionary Computation Conference 2023

Y2 - 15 July 2023 through 19 July 2023

ER -

ELEA – Build your own evolutionary algorithm in your browser

Abstract

Conference

Keywords

Access to Document

Other files and links

Cite this