Thema3435: Unterschied zwischen den Versionen
Ig (Diskussion | Beiträge) (Die Seite wurde neu angelegt: „{{Abschlussarbeit |Titel=Incorporating user preferences in Multi-objective evolutionary algorithms |Vorname=Marlon Alexander |Nachname=Braun |Abschlussarbeitstyp=…“) |
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|Nachname=Braun | |Nachname=Braun | ||
|Abschlussarbeitstyp=Bachelor | |Abschlussarbeitstyp=Bachelor | ||
| − | |Betreuer=Hartmut Schmeck; | + | |Betreuer=Pradyumn Kumar Shukla; Hartmut Schmeck; |
|Forschungsgruppe=Effiziente Algorithmen | |Forschungsgruppe=Effiziente Algorithmen | ||
|Abschlussarbeitsstatus=Abgeschlossen | |Abschlussarbeitsstatus=Abgeschlossen | ||
|Abgabe=2011/07/11 | |Abgabe=2011/07/11 | ||
| + | |Beschreibung DE=For many years evolutionary algorithms have shown to be capable of solving multi-objective | ||
| + | optimization problems. Nearly all of these algorithms aim at finding an approximation of | ||
| + | the Pareto optimal front, the set of solutions that are not dominated by any other selection | ||
| + | of decision variables. Considering user preferences, however, not all Pareto optimal solu- | ||
| + | tions necessarily present an interesting choice. The concept of proper Pareto optimality | ||
| + | tries to bound the trade-off between objective values. The notion has been successfully im- | ||
| + | plemented in deterministic search methods, but has so far only received little attention in | ||
| + | evolutionary optimization. This thesis proposes modifications of existing state-of-the-art | ||
| + | algorithms that guide the search towards proper Pareto optimal solutions. An extensive | ||
| + | set of different benchmark problems is used to assess the performance of these algorithms. | ||
| + | Computational results indicate that the modified algorithms are able to find the complete | ||
| + | solution set of preferred regions. | ||
}} | }} | ||
Version vom 6. Februar 2015, 15:13 Uhr
Abschlussarbeitstyp: Bachelor
Betreuer: Pradyumn Kumar Shukla, Hartmut Schmeck
Forschungsgruppe: Effiziente Algorithmen
Archivierungsnummer: 3435
Abschlussarbeitsstatus: Abgeschlossen
Beginn: unbekannt
Abgabe: 11. Juli 2011
For many years evolutionary algorithms have shown to be capable of solving multi-objective optimization problems. Nearly all of these algorithms aim at finding an approximation of the Pareto optimal front, the set of solutions that are not dominated by any other selection of decision variables. Considering user preferences, however, not all Pareto optimal solu- tions necessarily present an interesting choice. The concept of proper Pareto optimality tries to bound the trade-off between objective values. The notion has been successfully im- plemented in deterministic search methods, but has so far only received little attention in evolutionary optimization. This thesis proposes modifications of existing state-of-the-art algorithms that guide the search towards proper Pareto optimal solutions. An extensive set of different benchmark problems is used to assess the performance of these algorithms. Computational results indicate that the modified algorithms are able to find the complete solution set of preferred regions.