Multiobjective evolutionary algorithms have incorporated surrogate models in order to reduce the number of required evaluations to approximate the Pareto front of computationally expensive multiobjective optimization problems. Currently, few works have reviewed the state of the art in this topic. However, the existing reviews have focused on classifying the evolutionary multiobjective optimization algorithms with respect to the type of underlying surrogate model. In this paper, we center our focus on classifying multiobjective evolutionary algorithms with respect to their integration with surrogate models. This interaction has led us to classify similar approaches and identify advantages and disadvantages of each class.

The use of the Web has increased the creation of digital information in an accelerated way and about multiple subjects. Text classification is widely used to filter emails, classify web pages and organize results retrieved by web browsers. In this paper, we propose to raise the problem of automatic classification of scientific texts as an optimization problem, which will allow to obtain groups from a data set. The use of evolutionary algorithms to solve classification problems has been a recurrent approach. However, there are a few approaches in which classification problems are solved where the data attributes to be classified are text-type. In this way, it is proposed to use the Association for Computing Machinery (ACM) taxonomy to obtain the similarity between documents, where each document consists of a set of keywords. According to the results obtained, the algorithm is competitive, which indicates that the proposal of a knowledge-based genetic algorithm is a viable approach to solve the classification problem.