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|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 | + | |Booktitle=Parallel Problem Solving from Nature - PPSN XI, Part II |
| + | |Pages=131--140 | ||
|Publisher=Springer | |Publisher=Springer | ||
| + | |Address=Heidelberg | ||
| + | |Editor=Robert Schaefer and Carlos Cotta and Joanna Kolodziej and Günter Rudolph | ||
|Series=LNCS | |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. | ||
|Forschungsgruppe=Effiziente Algorithmen | |Forschungsgruppe=Effiziente Algorithmen | ||
}} | }} | ||
Version vom 29. Juli 2010, 08:12 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
Herausgeber: Robert Schaefer and Carlos Cotta and Joanna Kolodziej and Günter Rudolph
Buchtitel: Parallel Problem Solving from Nature - PPSN XI, Part II
Ausgabe: 6239
Reihe: LNCS
Seiten: 131--140
Verlag: Springer
Erscheinungsort: 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.