## 1 Introduction
In the absence of noisy annotation (mislabeling or inaccurate identification of the start and end times of each segment), the two methods achieve similar recognition performances on three data sets compared with DTW- and SVM-based methods and surpass them in the presence of mislabeled instances. Extensions were recently proposed, such as a multimodal system based on WarpingLCSS [19], S-SMART [20], and a limited memory and real-time version for resource constrained sensor nodes [21]. Although the parameters of these LCSS-based methods should be application-dependent, they have so far been empirically determined and a lack of design procedure (parameter-tuning methods) has been suggested. In designing mobile or wearable gesture recognition systems, the temptation of integrating many sensing units for handling complex gesture often negates key real-life deployment constraints, such as cost, power efficiency, weight limitations, memory usage, privacy, or unobtrusiveness [22]. The redundant or irrelevant dimensions introduced may even slow down the learning process and affect recognition performance. The most popular dimensionality reduction approaches include feature extraction (or construction), feature selection, and discretization. Feature extraction aims to generate a set of features from original data with a lower computational cost than using the complete list of dimensions. A feature selection method selects a subset of features from the original feature list. Feature selection is an NP-hard combinatorial problem [23]. Although numerous search techniques can be found in the literature, they fail to avoid local optima and require a large amount of memory or very long runtimes. Alternatively, evolutionary computation techniques have been proposed for solving feature selection problem [24]. Since the abovementioned LCSS technique directly utilizes raw or filtered signals, there is no evidence on whether we should favour feature extraction or selection. However, these LCSS-based methods impose the transformation of each sample from the data stream into a sequence of symbols. Therefore, a feature selection coupled with a discretization process could be employed. Similar to feature selection, discretization is also an NP-hard problem [25; 26]. In contrast to the feature selection field, few evolutionary algorithms are proposed in the literature [25; 27]. Indeed, evolutionary feature selection algorithms have the dis advantage of high computational cost [28] while convergence (close to the true Pareto front) and diversity of solutions (set of solutions as diverse as possible) are still two major difficulties [29]. Evolutionary feature selection methods focus on maximizing the classification performance and on minimizing the number of dimensions. Although it is not yet clear whether removing some features can lead to a decrease in classification error rate [24], a multiple-objective problem formulation could bring trade-offs. Discretization attribute literature aims to minimize the discretization scheme complexity and to maximize classification accuracy. In contrast to feature selection, these two objectives seem to be conflicting in nature [30]. A multi-objective optimization algorithm based on Particle swarm optimization (heuristic methods) can provide an optimal solution. However, an increase in feature quantities increases the solution space and then decreases the search efficiency [31]. Therefore, [PERSON] et al. 2021 [31] noted that particle swarm optimisation may find a local optimum with high dimensional data. Some variants are suggested such as competitive swarm optimization operator [32] and multiswarm comprehensive learning particle swarm optimization [33], but tackling many-objective optimization is still a challenge [29]. Moreover, particle swarm optimization can fall into a local optimum (needs a reasonable balance between convergence and diversity) [29]. Those results are similar to filter and wrapper methods [34] (more details about Filter and wrapper methods can be found in [31; 34]). [PERSON] et al.