Genetic algorithm for non-linear mixed integer programming problems and its applications

In this paper we propose a method for solving non-linear mixed integer programming (NMIP) problems using genetic algorithm (GAs) to get an optimal or near optimal solution. The penalty function method was used to evaluate those infeasible chromosomes generated from genetic reproduction. Also, we app...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Computers & industrial engineering Jg. 30; H. 4; S. 905 - 917
Hauptverfasser: Yokota, Takao, Gen, Mitsuo, Li, Yin-Xiu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier Ltd 01.09.1996
Pergamon Press Inc
Schlagworte:
Algorithms
Applications
Genetic algorithms
Industrial engineering
Integer programming
Nonlinear programming
Studies
ISSN:0360-8352, 1879-0550
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:In this paper we propose a method for solving non-linear mixed integer programming (NMIP) problems using genetic algorithm (GAs) to get an optimal or near optimal solution. The penalty function method was used to evaluate those infeasible chromosomes generated from genetic reproduction. Also, we apply the method for solving several optimization problems of system reliability which belong to non-linear integer programming (NIP) or (NMIP) problems, using the proposed method. Numerical experiments and comparisons with previous works are illustrated to demonstrate the efficiency of the proposed method.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
content type line 14
ISSN:0360-8352
1879-0550
DOI:10.1016/0360-8352(96)00041-1