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An empirical study of the crossover operator in a genetic algorithm used as a wrapper for feature selection
  • Mario Dudjak
Mario Dudjak
Sveuciliste Josipa Jurja Strossmayera u Osijeku Fakultet elektrotehnike racunarstva i informacijskih tehnologija

Corresponding Author:[email protected]

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Wrapper-based feature selection plays a pivotal role in data mining, operating to reduce dimensionality and identify relevant features within datasets. Given the computationally demanding nature of the wrapper’s search for intricate feature relationships, numerous bio-inspired algorithms have been employed to facilitate the process. Notably, the genetic algorithm stands out due to its representation of solutions as binary strings. The literature presents a multitude of genetic algorithm-based wrappers, predominantly employing the n-point crossover operator, where n conventionally takes values of 1 or 2. This study explores the impact of varying the parameter n in the n-point crossover operator on the efficacy of the wrapper’s search. The performed analysis underscores that no single parameter value prevails, motivating the need for dynamic adjustment during the search. Consequently, several elegant strategies for this purpose are proposed and meticulously evaluated, leveraging a comprehensive examination of the genetic algorithm’s convergence behavior. These strategies are experimentally compared with established crossover operators from the literature, leading to the identification of noteworthy discoveries. The empirical findings present a valuable resource for researchers and practitioners alike, poised to enhance feature selection processes within data mining applications. Keywords: bio-inspired optimization; classification; feature selection; genetic algorithm; n-point crossover; wrappers