• Evolutionary multi-objective o...

Treffer: Evolutionary multi-objective optimisation by diversity control

Title:
Evolutionary multi-objective optimisation by diversity control
Authors:
KULVANIT, Pasan, PIROONRATANA, Theera, CHAIYARATANA, Nachol, LAOWATTANA, Djitt
Source:
Computer science (theory and applications)0CSR 2006. :447-456
Publisher Information:
Berlin: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 14 ref 1
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, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Loi Pareto, Pareto distribution, Ley Pareto, Optimum Pareto, Pareto optimum, Optimo Pareto, Programmation multiobjectif, Multiobjective programming, Programación multiobjetivo, Solution optimale, Optimal solution, Solución óptima, Triage, Sorting, Tría, Uniformité, Uniformity, Uniformidad
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Institute of Field Robotics, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand
Research and Development Center for Intelligent Systems, King Mongkut's Institute of Technology North Bangkok, Bangkok 10800, Thailand
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=19150271
Rights:
Copyright 2007 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.19150271
Database:
PASCAL Archive

Weitere Informationen

This paper presents an improved multi-objective diversity control oriented genetic algorithm (MODCGA-II). The performance comparison between the MODCGA-II, a non-dominated sorting genetic algorithm II (NSGA-II) and an improved strength Pareto evolutionary algorithm (SPEA-II) is carried out where different two- and three-objective benchmark problems with specific multi-objective characteristics are used. The results indicate that the two-objective MODCGA-II solutions are better than the solutions generated by the NSGA-II and SPEA-II in terms of the closeness to the true Pareto optimal solutions and the uniformity of solution distribution along the Pareto front. In contrast, the NSGA-II in overall produces the best solutions in three-objective problems. As a result, the limitation of the proposed algorithm is identified.