# Guided Particle Swarm Optimization for Feature Selection: Application to Cancer Genome Data
## 2 Related Work
Results show that overall, the SIEAs outperform traditional FS methods, achieving higher accuracy and efficiency, especially in complex scenes. Particle Swarm Optimization (PSO) is also a popular evolutionary computation (EC) method that has been widely applied to feature selection problems. For example, an adaptive Particle Swarm Optimization (PSO) method for feature selection that overcomes the limitations of traditional PSO by incorporating adaptive parameter updating and leadership learning strategies is presented in [11]. Experimental results on 10 UCI datasets show that the proposed method outperforms other algorithms in both exploration and exploitation, selecting fewer than 8% of the original features while achieving more effective feature subsets than six traditional feature selection methods. Binary Particle Swarm Optimization (BPSO) is a discrete variant of the Particle Swarm Optimization (PSO) algorithm, designed to handle optimization problems where variables are binary (i.e., they can take on values of 0 or 1). In traditional PSO, particles adjust their positions and velocities in a continuous search space to find optimal solutions. However, BPSO modifies this approach to operate within a binary search space. In BPSO, each particle represents a candidate solution as a binary string. The concept of velocity is reinterpreted as the probability of each bit in the string flipping from 0 to 1 or vice versa. This probability is typically determined using a sigmoid function applied to the velocity component, ensuring that updates remain within the binary constraints [12]. A Stick Binary PSO (BPSO), redefining momentum as stickiness and velocity as flipping probability, was introduced in [13]. The stickiness factor considers the stability of particle states, encouraging particles that have remained unchanged for a long period to flip, thereby increasing population diversity. However, unconstrained flipping in the time dimension expands the search space, increasing computational costs and slowing population convergence. An applied PSO within an evolutionary multitasking framework Is featured in [14]. The first task involved selecting from all original features, while the second focused on choosing from only the top-ranked features. Similarly, authors in [15] employed BPSO for spam detection, introducing mutation operators to mitigate premature convergence and improve algorithm performance. Their experimental results demonstrated superior performance compared to other methods. The ChaoticMap algorithm integrates two types of chaotic maps (logistic maps and tent maps) into the Binary PSO (BPSO) [16]. The chaotic maps are used to determine the inertia weight of the BPSO. The Chaotic Binary Particle Swarm Optimization (CBPSO) for feature selection employs the K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) as a classifier to evaluate classification accuracy. The proposedfeature selection method yields promising results in terms of reducing the number of feature subsets while achieving superior classification accuracy compared to other methods in the literature. VLPSO, a PSO variant with dynamic and variable-length feature selection, which achieved higher classification accuracy in less time, was introduced in [17]. Additionally, the Competitive Swarm Optimization (CSO) algorithm was introduced to explore novel optimization strategies [18]. [PERSON] et al. further enhanced CSO by incorporating performance constraints and the Relief algorithm to improve population diversity and local search efficiency [19]. They also employed SVM as a surrogate model to accelerate evaluations. Their results showed that adaptive performance constraints guided particles toward high-quality solutions, but this approach increased exploitation tendencies, making the population more prone to local optima. From this review, so far, it is evident that existing PSO-based feature selection algorithms largely rely on mathematical and evolutionary computation methods for population initialization and update strategies.