Optimization of multimodal continuous functions using a new crossover for the real-coded genetic algorithms

In this study, a new crossover approach to the real-coded genetic algorithm is proposed. The approach is simply based on efficiently tuned real-coded crossover operation using the probability distribution function of Gauss distribution to generate rather dissimilar strings which may be candidates of possible solutions. Also linear and quadratic mapping algorithms comparatively used both to constrain individuals in the given search spaces and to produce different individuals in order to increase average fitness relatively for the same population. Moreover, to refine genetically found optimum points the local search technique based on Newton's method was performed. The designed software was first implemented on 11 well-known test functions and their results were compared with previous findings as shown in tables. In few test functions, the elitism operator was put into effect to maintain fitness stability helping increase the search performance of the proposed algorithm. The results indicate that the solutions to the test functions were almost the same with theoretical ones and the number of function evaluations for each test function was less than that obtained from using previous approaches. © 2008 Elsevier Ltd. All rights reserved.

Dergi Adı Expert Systems with Applications
Dergi Cilt Bilgisi 36
Dergi Sayısı 4
Sayfalar 8172 - 8177
Yayın Yılı 2009
Eser Adı
[dc.title]
Optimization of multimodal continuous functions using a new crossover for the real-coded genetic algorithms
Yazar
[dc.contributor.author]
Tutkun N.
Yayın Yılı
[dc.date.issued]
2009
Yayın Türü
[dc.type]
article
Özet
[dc.description.abstract]
In this study, a new crossover approach to the real-coded genetic algorithm is proposed. The approach is simply based on efficiently tuned real-coded crossover operation using the probability distribution function of Gauss distribution to generate rather dissimilar strings which may be candidates of possible solutions. Also linear and quadratic mapping algorithms comparatively used both to constrain individuals in the given search spaces and to produce different individuals in order to increase average fitness relatively for the same population. Moreover, to refine genetically found optimum points the local search technique based on Newton's method was performed. The designed software was first implemented on 11 well-known test functions and their results were compared with previous findings as shown in tables. In few test functions, the elitism operator was put into effect to maintain fitness stability helping increase the search performance of the proposed algorithm. The results indicate that the solutions to the test functions were almost the same with theoretical ones and the number of function evaluations for each test function was less than that obtained from using previous approaches. © 2008 Elsevier Ltd. All rights reserved.
Kayıt Giriş Tarihi
[dc.date.accessioned]
2019-12-23
Açık Erişim Tarihi
[dc.date.available]
2019-12-23
Yayın Dili
[dc.language.iso]
eng
Konu Başlıkları
[dc.subject]
Design optimization
Konu Başlıkları
[dc.subject]
Function minimization
Konu Başlıkları
[dc.subject]
Genetic algorithms
Konu Başlıkları
[dc.subject]
Stochastic optimization
Haklar
[dc.rights]
info:eu-repo/semantics/closedAccess
ISSN
[dc.identifier.issn]
0957-4174
İlk Sayfa Sayısı
[dc.identifier.startpage]
8172
Son Sayfa Sayısı
[dc.identifier.endpage]
8177
Dergi Adı
[dc.relation.journal]
Expert Systems with Applications
Dergi Sayısı
[dc.identifier.issue]
4
Dergi Cilt Bilgisi
[dc.identifier.volume]
36
Tek Biçim Adres
[dc.identifier.uri]
https://dx.doi.org/10.1016/j.eswa.2008.10.042
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/6874
Görüntülenme Sayısı ( Şehir )
Görüntülenme Sayısı ( Ülke )
Görüntülenme Sayısı ( Zaman Dağılımı )
Görüntülenme
67
09.12.2022 tarihinden bu yana
İndirme
1
09.12.2022 tarihinden bu yana
Son Erişim Tarihi
25 Mayıs 2024 08:59
Google Kontrol
Tıklayınız
search functions function individuals increase fitness previous results distribution proposed crossover approach real-coded algorithm solutions evaluations findings obtained compared rights reserved well-known implemented software number stability theoretical almost approaches designed indicate performance helping maintain effect
6698 sayılı Kişisel Verilerin Korunması Kanunu kapsamında yükümlülüklerimiz ve çerez politikamız hakkında bilgi sahibi olmak için alttaki bağlantıyı kullanabilirsiniz.

creativecommons
Bu site altında yer alan tüm kaynaklar Creative Commons Alıntı-GayriTicari-Türetilemez 4.0 Uluslararası Lisansı ile lisanslanmıştır.
Platforms