Choosing Representation, Mutation, and Crossover in Genetic Algorithms

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autoren

Externe Organisationen

  • Queen Mary University of London
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Seiten (von - bis)52-53
Seitenumfang2
FachzeitschriftIEEE Computational Intelligence Magazine
Jahrgang17
Ausgabenummer4
PublikationsstatusVeröffentlicht - 1 Nov. 2022

Abstract

This paper aims to provide an introduction to genetic algorithms and their three main components, i.e., the representation of solutions and their modification through mutation and crossover operators. It has been specifically designed as introduction for newcomers to this exciting research area. This short paper represents a summary of the full paper found online in IEEE Xplore. The latter provides interactive components for a hands-on exploration of the covered material.

ASJC Scopus Sachgebiete

Zitieren

Choosing Representation, Mutation, and Crossover in Genetic Algorithms. / Dockhorn, Alexander; Lucas, Simon.
in: IEEE Computational Intelligence Magazine, Jahrgang 17, Nr. 4, 01.11.2022, S. 52-53.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Download
@article{d6e4a056f4174bb9af400757280d2cf2,
title = "Choosing Representation, Mutation, and Crossover in Genetic Algorithms",
abstract = "This paper aims to provide an introduction to genetic algorithms and their three main components, i.e., the representation of solutions and their modification through mutation and crossover operators. It has been specifically designed as introduction for newcomers to this exciting research area. This short paper represents a summary of the full paper found online in IEEE Xplore. The latter provides interactive components for a hands-on exploration of the covered material.",
author = "Alexander Dockhorn and Simon Lucas",
year = "2022",
month = nov,
day = "1",
doi = "10.1109/MCI.2022.3199626",
language = "English",
volume = "17",
pages = "52--53",
journal = "IEEE Computational Intelligence Magazine",
issn = "1556-603X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "4",

}

Download

TY - JOUR

T1 - Choosing Representation, Mutation, and Crossover in Genetic Algorithms

AU - Dockhorn, Alexander

AU - Lucas, Simon

PY - 2022/11/1

Y1 - 2022/11/1

N2 - This paper aims to provide an introduction to genetic algorithms and their three main components, i.e., the representation of solutions and their modification through mutation and crossover operators. It has been specifically designed as introduction for newcomers to this exciting research area. This short paper represents a summary of the full paper found online in IEEE Xplore. The latter provides interactive components for a hands-on exploration of the covered material.

AB - This paper aims to provide an introduction to genetic algorithms and their three main components, i.e., the representation of solutions and their modification through mutation and crossover operators. It has been specifically designed as introduction for newcomers to this exciting research area. This short paper represents a summary of the full paper found online in IEEE Xplore. The latter provides interactive components for a hands-on exploration of the covered material.

UR - http://www.scopus.com/inward/record.url?scp=85142308625&partnerID=8YFLogxK

U2 - 10.1109/MCI.2022.3199626

DO - 10.1109/MCI.2022.3199626

M3 - Article

AN - SCOPUS:85142308625

VL - 17

SP - 52

EP - 53

JO - IEEE Computational Intelligence Magazine

JF - IEEE Computational Intelligence Magazine

SN - 1556-603X

IS - 4

ER -

Von denselben Autoren