Result: Indicator-based selection in multiobjective search

Title:
Indicator-based selection in multiobjective search
Authors:
ZITZLER, Eckart, KÜNZLI, Simon
Source:
PPSN VIII : parallel problem solving from nature (Birmingham, 18-22 September 2004)Lecture notes in computer science. :832-842
Publisher Information:
Berlin: Springer, 2004.
Publication Year:
2004
Physical Description:
print, 18 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, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Biomimétique, Biomimetics, Choix procédé, Process selection, Selección proceso, Comportement utilisateur, User behavior, Comportamiento usuario, Optimisation, Optimization, Optimización, Parallélisme, Parallelism, Paralelismo, Programmation multiobjectif, Multiobjective programming, Programación multiobjetivo, Préférence, Preference, Preferencia, Préservation, Preservation, Preservación, Résolution problème, Problem solving, Resolución problema
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Swiss Federal Institute of Technology Zurich, Computer Engineering and Networks Laboratory (TIK), Gloriastrasse 35, 8092 Zürich, Switzerland
ISSN:
0302-9743
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=16177151
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.16177151
Database:
PASCAL Archive

Further Information

This paper discusses how preference information of the decision maker can in general be integrated into multiobjective search. The main idea is to first define the optimization goal in terms of a binary performance measure (indicator) and then to directly use this measure in the selection process. To this end, we propose a general indicator-based evolutionary algorithm (IBEA) that can be combined with arbitrary indicators. In contrast to existing algorithms, IBEA can be adapted to the preferences of the user and moreover does not require any additional diversity preservation mechanism such as fitness sharing to be used. It is shown on several continuous and discrete benchmark problems that IBEA can substantially improve on the results generated by two popular algorithms, namely NSGA-II and SPEA2, with respect to different performance measures.