Niching by multiobjectivization with neighbor information: Trade-offs and benefits

Wessing S., Preuss M., Rudolph G.


Zusammenfassung
In this paper we investigate the ability of selection methods to enforce niching on multimodal problems. Using theoretical properties where possible, and relying on a sound experimental analysis, we show that the conventional single-objective optimization and novelty search are extreme cases of selection, striving only for quality or diversity. However, in between these well known cases, there are many more possibilities, of which we review eight (including the aforementioned two). Multiobjective selection approaches provide a well-balanced trade-off between exploration and exploitation. For the multiobjectivization, we recommend to use nearest-better-neighbor information instead of the common nearest-neighbor approaches. © 2013 IEEE.



Publikationstyp
Aufsatz (Konferenz)

Begutachtet
Ja

Publikationsstatus
Veröffentlicht

Jahr
2013

Konferenz
2013 IEEE Congress on Evolutionary Computation, CEC 2013

Konferenzort
Cancun, mex

Erste Seite
103

Letzte Seite
110

Seiten
103-110

Band
null

Sprache
Englisch

ISBN
9781479904549

DOI

Gesamter Text

Affiliierungen
Universitat Dortmund