## 1 Introduction
2020 [29] suggest to improve computational burdens with a competition mechanism using a new environment selection strategy to maintain the diversity of population. Additionally, to solve this issue, since mutual information can capture nonlinear relationships included in a filter approach, [PERSON] et al. 2019 [35] used mutual information as a selection criteria (joint bias-corrected mutual information) and then suggested adding simultaneous forward selection and backward elimination [36]. Deep neural networks such as CNN [37] are able to learn and select features. As an example, hierarchical deep neural networks were included with a multiobjective model to learn useful sparse features [38]. Due to the huge number of parameter, a deep learning approach needs a high quantity of balanced samples, which is sometimes not satisfied in real-world problems [34]. Moreover, as a deep neural network is a black box (non-causal and non-explicable), an evaluation of the feature selection ability is difficult [37]. Currently, feature selection and data discretization are still studied individually and not fully explored [39] using many-objective formulation. To the best of our knowledge, no studies have tried to solve the two problems simultaneously using evolutionary techniques for a many-objective formulation. In this paper, the contributions are summarized as follows:  
1. We propose a many-objective formulation to simultaneously deal with optimal feature subset selection, discretization, and parameter tuning for an LM-WLCSS classifier. This problem was resolved using the constrained many-objective evolutionary algorithm based on dominance (minimisation of the objectives) and decomposition (C-MOEA/DD) [40]. 2. Unlike many discretization techniques requiring a prefixed number of discretization points, the proposed discretization subproblem exploits a variable-length representation [41]. 3. To agree with the variable-length discretization structure, we adapted the recently proposed rand-length crossover to the random variable-length crossover differential evolution algorithm [42]. 4. We refined the template construction phase of the microcontroller optimized Limited-Memory WarpingLCSS (LM-WLCSS) [21] using an improved algorithm for computing the longest common subsequence [43]. Moreover, we altered the recognition phase by reprocessing the samples contained in the sliding windows in charge of spotting a gesture in the steam. To tackle multiclass gesture recognition, we propose a system encapsulating multiple LM-WLCSS and a light-weight classifier for resolving conflicts. The main hypothesis is as follows: using the constrained many-objective evolutionary algorithm based on dominance, an optimal feature subset selection can be found. The rest of the paper is organized as follows: Section 2 states the constrained many-objective optimization problem definition, exposes C-MOEA/DD, highlights some discretization works, presents our refined LM-WLCSS, and reviews multiple fusion methods based on WarpingLCSS. Our solution encoding, operators, objective functions, and constraints are presented in Section 3. Subsequently, we present the decision fusion module. The experiments are described in Section 4 with the methodology and their corresponding evaluation metrics (two for effectiveness, including Cohen's kappa, and one for reduction). Finally, our system is evaluated and the results are discussed in Section 5.