  Multi-objective feature selection is one of the most significant issues in
the field of pattern recognition. It is challenging because it maximizes the
classification performance and, at the same time, minimizes the number of
selected features, and the mentioned two objectives are usually conflicting. To
achieve a better Pareto optimal solution, metaheuristic optimization methods
are widely used in many studies. However, the main drawback is the exploration
of a large search space. Another problem with multi-objective feature selection
approaches is the interaction between features. Selecting correlated features
has negative effect on classification performance. To tackle these problems, we
present a novel multi-objective feature selection method that has several
advantages. Firstly, it considers the interaction between features using an
advanced probability scheme. Secondly, it is based on the Pareto Archived
Evolution Strategy (PAES) method that has several advantages such as simplicity
and its speed in exploring the solution space. However, we improve the
structure of PAES in such a way that generates the offsprings, intelligently.
Thus, the proposed method utilizes the introduced probability scheme to produce
more promising offsprings. Finally, it is equipped with a novel strategy that
guides it to find the optimum number of features through the process of
evolution. The experimental results show a significant improvement in finding
the optimal Pareto front compared to state-of-the-art methods on different
real-world datasets.
