Inproceedings3072: Unterschied zwischen den Versionen
Chi (Diskussion | Beiträge) (Die Seite wurde neu angelegt: „{{Publikation Erster Autor |ErsterAutorNachname=Shukla |ErsterAutorVorname=Pradyumn }} {{Publikation Author |Rank=2 |Author=Christian Hirsch }} {{Publikation Auth…“) |
Sme (Diskussion | Beiträge) |
||
(5 dazwischenliegende Versionen von 2 Benutzern werden nicht angezeigt) | |||
Zeile 1: | Zeile 1: | ||
{{Publikation Erster Autor | {{Publikation Erster Autor | ||
|ErsterAutorNachname=Shukla | |ErsterAutorNachname=Shukla | ||
− | |ErsterAutorVorname=Pradyumn | + | |ErsterAutorVorname=Pradyumn Kumar |
}} | }} | ||
{{Publikation Author | {{Publikation Author | ||
Zeile 15: | Zeile 15: | ||
|Title=A Framework for Incorporating Trade-off Information Using Multi-objective Evolutionary Algorithms | |Title=A Framework for Incorporating Trade-off Information Using Multi-objective Evolutionary Algorithms | ||
|Year=2010 | |Year=2010 | ||
− | |Booktitle=Parallel Problem Solving from Nature - PPSN XI | + | |Month=September |
+ | |Booktitle=Parallel Problem Solving from Nature - PPSN XI, Part II | ||
+ | |Pages=131-140 | ||
|Publisher=Springer | |Publisher=Springer | ||
+ | |Address=Berlin Heidelberg | ||
+ | |Editor=Robert Schaefer, Carlos Cotta, Joanna Kolodziej, Günter Rudolph | ||
+ | |Series=LNCS | ||
+ | |Volume=6239 | ||
}} | }} | ||
{{Publikation Details | {{Publikation Details | ||
+ | |Abstract=Since their inception,multi-objective evolutionary algorithms have been adequately applied in finding a diverse approximation of efficient fronts of multi-objective optimization problems. In contrast, if we look at the rich history of classical multi-objective algorithms, we find that incorporation of user preferences has always been a major thrust of research. In this paper, we provide a general structure for incorporating preference information using multi-objective evolutionary algorithms. This is done in an NSGA-II scheme and by considering trade-off based preferences that come from so called proper Pareto-optimal solutions. We argue that finding proper Pareto-optimal solutions requires a set to compare with and hence, population based approaches should be a natural choice. Moreover, we suggest some practical modifications to the classical notion of proper | ||
+ | Pareto-optimality. Computational studies on a number of test problems of varying complexity demonstrate the efficiency of multi-objective evolutionary algorithms in finding the complete preferred region for a large class of complex problems. We also discuss a theoretical justification for our NSGA-II based framework. | ||
+ | |ISBN=978-3-642-15870-4 | ||
|Forschungsgruppe=Effiziente Algorithmen | |Forschungsgruppe=Effiziente Algorithmen | ||
}} | }} |
Aktuelle Version vom 15. März 2011, 13:52 Uhr
A Framework for Incorporating Trade-off Information Using Multi-objective Evolutionary Algorithms
A Framework for Incorporating Trade-off Information Using Multi-objective Evolutionary Algorithms
Published: 2010
September
Herausgeber: Robert Schaefer, Carlos Cotta, Joanna Kolodziej, Günter Rudolph
Buchtitel: Parallel Problem Solving from Nature - PPSN XI, Part II
Ausgabe: 6239
Reihe: LNCS
Seiten: 131-140
Verlag: Springer
Erscheinungsort: Berlin Heidelberg
Referierte Veröffentlichung
BibTeX
Kurzfassung
Since their inception,multi-objective evolutionary algorithms have been adequately applied in finding a diverse approximation of efficient fronts of multi-objective optimization problems. In contrast, if we look at the rich history of classical multi-objective algorithms, we find that incorporation of user preferences has always been a major thrust of research. In this paper, we provide a general structure for incorporating preference information using multi-objective evolutionary algorithms. This is done in an NSGA-II scheme and by considering trade-off based preferences that come from so called proper Pareto-optimal solutions. We argue that finding proper Pareto-optimal solutions requires a set to compare with and hence, population based approaches should be a natural choice. Moreover, we suggest some practical modifications to the classical notion of proper
Pareto-optimality. Computational studies on a number of test problems of varying complexity demonstrate the efficiency of multi-objective evolutionary algorithms in finding the complete preferred region for a large class of complex problems. We also discuss a theoretical justification for our NSGA-II based framework.
ISBN: 978-3-642-15870-4