Result: On test functions for evolutionary multi-objective optimization
Title:
On test functions for evolutionary multi-objective optimization
Authors:
Source:
PPSN VIII : parallel problem solving from nature (Birmingham, 18-22 September 2004)Lecture notes in computer science. :792-802
Publisher Information:
Berlin: Springer, 2004.
Publication Year:
2004
Physical Description:
print, 9 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 évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Biomimétique, Biomimetics, Fonction densité probabilité, Probability density function, Función densidad probabilidad, Fonction objectif, Objective function, Función objetivo, Linéarisation morceau, Piecewise linearization, Linearización trozo, Optimisation, Optimization, Optimización, Optimum Pareto, Pareto optimum, Optimo Pareto, Parallélisme, Parallelism, Paralelismo, Performance algorithme, Algorithm performance, Resultado algoritmo, Programmation mathématique, Mathematical programming, Programación matemática, Programmation multiobjectif, Multiobjective programming, Programación multiobjetivo, Résolution problème, Problem solving, Resolución problema
Document Type:
Conference
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Honda Research Institute Europe GmbH, Carl-Legien-Strasse 30, 63073 Offenbach/Main, Germany
ISSN:
0302-9743
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
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.16176916
Database:
PASCAL Archive
Further Information
In order to evaluate the relative performance of optimization algorithms benchmark problems are frequently used. In the case of multi-objective optimization (MOO), we will show in this paper that most known benchmark problems belong to a constrained class of functions with piecewise linear Pareto fronts in the parameter space. We present a straightforward way to define benchmark problems with an arbitrary Pareto front both in the fitness and parameter spaces. Furthermore, we introduce a difficulty measure based on the mapping of probability density functions from parameter to fitness space. Finally, we evaluate two MOO algorithms for new benchmark problems.