Niching by multiobjectivization with neighbor information: Trade-offs and benefits
Abstract
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.
Cite as
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
Publication type
Research article in proceedings (conference)
Peer reviewed
Yes
Publication status
Published
Year
2013
Conference
2013 IEEE Congress on Evolutionary Computation, CEC 2013
Venue
Cancun, mex
Start page
103
End page
110
Volume
null
Language
English
ISBN
9781479904549
DOI
Full text