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

Roberto Santana, Hossein Karshenas, Concha Bielza, Pedro Larrañaga

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).