Quantitative genetics in multi-objective optimization algorithms: From useful insights to effective methods

Quantitative genetics in multi-objective optimization algorithms: From useful insights to effective methods” by R. Santana, H. Karshenas, C. Bielza, and P. Larrañaga. In Proceedings of the 2011 Genetic and Evolutionary Computation Conference GECCO-2011, (Dublin, Ireland), 2011, pp. 91-92.

Abstract

This paper shows that statistical algorithms proposed for the quantitative trait loci (QTL) mapping problem, and the equation of the multivariate response to selection can be of application in multi-objective optimization. We introduce the conditional dominance relationships between the objectives and propose the use of results from QTL analysis and G-matrix theory to the analysis of multi-objective evolutionary algorithms (MOEAs).

BibTeX entry:

@inproceedings{Santana_et_al:2011d,
   author = {R. Santana and H. Karshenas and C. Bielza and P. Larra{\~n}aga},
   title = {Quantitative genetics in multi-objective optimization
	algorithms: From useful insights to effective methods},
   booktitle = {Proceedings of the 2011 Genetic and Evolutionary
	Computation Conference GECCO-2011},
   pages = {91-92},
   address = {Dublin, Ireland},
   year = {2011},
   url = {http://dl.acm.org/citation.cfm?id=2001911}
}

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