An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems

By Hossam Faris, Majdi M. Mafarja, Ali Asghar Heidari, Ibrahim Aljarah, Ala’ M. Al-Zoubi, Seyedali Mirjalili, Hamido Fujita. Knowledge-Based Systems, 2018

595 citations recorded by the source corpus.

Abstract Searching for the (near) optimal subset of features is a challenging problem in the process of feature selection (FS). In the literature, Swarm Intelligence (SI) algorithms show superior performance in solving this problem. This motivated our attempts to test the performance of the newly proposed Salp Swarm Algorithm (SSA) in this area. As such, two new wrapper FS approaches that use SSA as the search strategy are proposed. In the first approach, eight transfer functions are employed to convert the continuous version of SSA to binary. In the second approach, the crossover operator is used in addition to the transfer functions to replace the average operator and enhance the exploratory behavior of the algorithm. The proposed approaches are benchmarked on 22 well-known UCI datasets and the results are compared with 5 FS methods: Binary Grey Wolf Optimizer (BGWO), Binary Gravitational Search Algorithms (BGSA), Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), and Genetic Algorithm (GA). The paper also considers an extensive study of the parameter setting for the proposed technique. From the results, it is observed that the proposed approach significantly outperforms others on around 90% of the datasets.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/abd625abd98a3100ffb3ef68296253b7546230e0

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Citations in the corpus

An improved grey wolf optimizer for solving engineering problems (2021, methodology): “…A. Mohamed et al., 2017; Nuaekaew et al., 2017), chip design (Fard et al., 2014; Venkataraman et al., 2020), feature selection (Arora et al., 2019; Faris, Mafarja, et al., 2018; Mafarja et al., 2019; Mafarja et al., 2018; Taghian et al., 2019a, 2019b; Taradeh et al., 2019), diseases diagnosis…”
Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019) (2021, methodology): “[214] developed an efficient binary SSA using S andV-shaped transfer functions in which a crossover operator was embedded in place of the average operator to enhance the exploration quality.”
A survey on new generation metaheuristic algorithms (2019, background): “The inspiration is the swarm behavior of salp during their navigation and food search in oceans (Faris et al., 2018).”
Salp swarm algorithm: a comprehensive survey (2019, methodology): “In [42], the authors proposed two wrapper feature selection methods that used the SSA as a binary search strategy.”