Evolutionary Population Dynamics and Grasshopper Optimization approaches for feature selection problems

By Majdi M. Mafarja, Ibrahim Aljarah, Ali Asghar Heidari, Abdelaziz I. Hammouri, Hossam Faris, Ala’ M. Al-Zoubi, Seyedali Mirjalili. Knowledge-Based Systems, 2017

390 citations recorded by the source corpus.

Abstract Searching for the optimal subset of features is known as a challenging problem in feature selection process. To deal with the difficulties involved in this problem, a robust and reliable optimization algorithm is required. In this paper, Grasshopper Optimization Algorithm (GOA) is employed as a search strategy to design a wrapper-based feature selection method. The GOA is a recent population-based metaheuristic that mimics the swarming behaviors of grasshoppers. In this work, an efficient optimizer based on the simultaneous use of the GOA, selection operators, and Evolutionary Population Dynamics (EPD) is proposed in the form of four different strategies to mitigate the immature convergence and stagnation drawbacks of the conventional GOA. In the first two approaches, one of the top three agents and a randomly generated one are selected to reposition a solution from the worst half of the population. In the third and fourth approaches, to give a chance to the low fitness solutions in reforming the population, Roulette Wheel Selection (RWS) and Tournament Selection (TS) are utilized to select the guiding agent from the first half. The proposed GOA_EPD approaches are employed to tackle various feature selection tasks. The proposed approaches are benchmarked on 22 UCI datasets. The comprehensive results and various comparisons reveal that the EPD has a remarkable impact on the efficacy of the GOA and using the selection mechanism enhanced the capability of the proposed approach to outperform other optimizers and find the best solutions with improved convergence trends. Furthermore, the comparative experiments demonstrate the superiority of the proposed approaches when compared to other similar methods in the literature.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/3dc2c52d3e1804a61c66d743d09f0e0160bf632d

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Citations in the corpus

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019) (2021, methodology): “[209] proposed GOA_EPD algorithm by using evolutionary population dynamic, roulette wheel and tournament selection for guiding the agent.”
An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems (2018, methodology): “Moreover, many SI-based FS approaches have been proposed in literature such as Genetic Algorithm (GA)-based FS [50, 51, 52], Gravitational Search Algorithm (GSA) [53, 54], DE [55, 56], Harmony Search (HS) [57], Bat Algorithm (BA) [58], Binary Grasshopper Optimization Algorithm (BGOA) [59], Binary Fi”
A survey on new generation metaheuristic algorithms (2019, methodology): “Mirjalili (2016a) proposes swarm intelligence optimization technique Dragonfly Algorithm (KS and Murugan, 2017).”