Multi-aggregator fuzzy decision trees: EC-based optimization

We introduce a generic, multicriteria model based on fuzzy logic concepts for decision support systems. Our goal is to build such a model by 1) fitting real-world data and 2) representing the preferences of specific-domain users or experts. Toward this end, we used evolutionary computation techniques. Initially, we worked on a first order aggregation model and performed its learning using genetic algorithms. This has been used in a specific application related to university admissions. Then, we propose a more advanced multiaggregation model based on decision trees. For the learning process of this model, we developed a technique inspired from genetic programming.