Decision-Based Questionnaire Systems

Abstract

In this paper, we develop a decision-based questionnaire system. Toward this end, we use evolutionary computation techniques. Initially, we work on a first order aggregation model and performed its learning using genetic algorithms, in which these preferences will be represented by a weighting vector associated with the variables involved in the aggregation process. In this model tree nodes represent aggregators, terminals or leaves correspond to variables, and weight values are added to the children branches for each aggregator. The parameters characterizing this multi-aggregation model are aggregators, weights and their combination in form of a tree structure. In this case, the learning process has to find the optimal combination of these parameters based on training data. In this learning process, the evolution principle remains the same as in a conventional GP but the DNA encoding needs to be defined according to the considered problem