Treffer: A proposal of an efficient crossover using fitness prediction and its application

Title:
A proposal of an efficient crossover using fitness prediction and its application
Authors:
MUTOH, Atsuko, NAKAMURA, Tsuyoshi, KATO, Shohei, ITOH, Hidenori
Source:
AI 2003 : advances in artificial intelligence (Perth, 3-5 December 2003)Lecture notes in computer science. :112-124
Publisher Information:
Berlin: Springer, 2003.
Publication Year:
2003
Physical Description:
print, 14 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme recherche, Search algorithm, Algoritmo búsqueda, Diagnostic, Diagnosis, Diagnóstico, Méthode combinatoire, Combinatorial method, Método combinatorio, Optimisation combinatoire, Combinatorial optimization, Optimización combinatoria, Optimum, Optimo, Temps exécution, Execution time, Tiempo ejecución
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya 466-8555, Japan
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=15508996
Rights:
Copyright 2004 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.15508996
Database:
PASCAL Archive

Weitere Informationen

Genetic algorithm (GA) is an effective method of solving combinatorial optimization problems. Generally speaking most of search algorithms require a large execution time in order to calculate some evaluation value. Crossover is very important in GA because discovering a good solution efficiently requires that the good characteristics of the parent individuals be recombined. The Multiple Crossover Per Couple (MCPC) is a method that permits a variable number of children for each mating pair, and MCPC generates a huge search space. Thus this method requires a huge amount of execution time to find a good solution. This paper proposes a novel approach to reduce time needed for fitness evaluation by prenatal diagnosis using fitness prediction. In the experiments based on actual problems, the proposed method found an optimum solution 50% faster than the conventional method did. The experimental results from standard test functions show that the proposed method using the Distributed Genetic Algorithm is applicable to other problems as well.