Multi-criteria sorting methods to select virtual peach ideotypes

Abstract

The model-based design of virtual fruit ideotypes using multi-objective optimisation algorithms could produce a high number of contrasted fruits. The breeder (decision-maker) will need an automatic tool allowing him/her to sort these contrasted ideotypes into predefined categories corresponding to several targeted traits. This paper aims to develop such a decision-making module to sort a set of fruit ideotypes into one of five preference-ordered categories in the context of brown rot-peach fruit pathosystem. First, a set of ideotypes with contrasted trade-off between three criteria was produced using multi-objective optimisation algorithms. Then, two multi-criteria decision-making methods (ELECTRE-Tri and DRSA: dominance-based rough set approach) were tested in order to reproduce the classification made by the decision-maker. Such a non-typical classification seemed difficult to be reproduced by the ELECTRE-TRI method while the decision rule-based method gave very good results (only 10% wrong assignments). The proposed decision-making tool is very useful to speed-up the model-based design of fruit ideotypes, i.e., breeding.