Please use this identifier to cite or link to this item: https://doi.org/10.1007/s10462-004-5900-6
Title: Evolving dynamic multi-objective optimization problems with objective replacement
Authors: Guan, S.-U. 
Chen, Q.
Mo, W.
Keywords: Multi-objective genetic algorithms
Multi-objective optimization
Multi-objective problems
Non-stationary environment
Issue Date: May-2005
Source: Guan, S.-U., Chen, Q., Mo, W. (2005-05). Evolving dynamic multi-objective optimization problems with objective replacement. Artificial Intelligence Review 23 (3) : 267-293. ScholarBank@NUS Repository. https://doi.org/10.1007/s10462-004-5900-6
Abstract: This paper studies the strategies for multi-objective optimization in a dynamic environment. In particular we focus on problems with objective replacement where some objectives may be replaced with new objectives during evolution. It is shown that the Pareto-optimal sets before and after the objective replacement share some common members. Based on this observation we suggest the inheritance strategy. When objective replacement occurs this strategy selects good chromosomes according to the new objective set from the solutions found before objective replacement and then continues to optimize them via evolution for the new objective set. The experiment results showed that this strategy can help MOGAs achieve better performance than MOGAs without using the inheritance strategy where the evolution is restarted when objective replacement occurs. More solutions with better quality are found during the same time span. © Springer 2005.
Source Title: Artificial Intelligence Review
URI: http://scholarbank.nus.edu.sg/handle/10635/55938
ISSN: 02692821
DOI: 10.1007/s10462-004-5900-6
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