Niching by multiobjectivization with neighbor information: Trade-offs and benefits
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.
Zitieren als
Wessing, S., Preuss, M., & Rudolph, G. (2013). Niching by multiobjectivization with neighbor information: Trade-offs and benefits. In Proceedings of the 2013 IEEE Congress on Evolutionary Computation, CEC 2013, Cancun, mex, 103–110.Details
Publikationstyp
Forschungsartikel in Sammelband (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
Band
null
Sprache
Englisch
ISBN
9781479904549
DOI
Gesamter Text