Feature Selection Using Multi-Objective Modified Genetic Algorithm in Multimodal Biometric System

Karthiga, R.; Mangai, S.

doi:10.1007/s10916-019-1351-0

Cited by 15 publications

(4 citation statements)

References 25 publications

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“…e network coverage after noninterference adaptive clustering is also analyzed [16]. e multiparameter weighted adaptive clustering algorithms are also proposed to locate the cluster head appropriately [17].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Clustering Algorithm for IIoT Based Mobile Opportunistic Networks

Zhao

Akter

2022

Security and Communication Networks

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The clustering algorithms play a crucial role for energy saving solutions in mobile opportunistic networks. If the selection of cluster head is made appropriately, then the energy can be consumed optimally. The existing clustering algorithms do not consider the optimal selection of the cluster head resulting in low survival rates and high energy consumption rates in nodes. The adaptive clustering is required in Industrial Internet of Things (IIoT) based sophisticated networks where seamless connectivity is imperative for rapid communication. In order to meet this research gap, an adaptive clustering algorithm for mobile opportunistic networks is proposed within military bases that uses a heuristic algorithm (GA) for adaptive clustering. An analysis of the opportunity network at the connected military base is carried out and the mobile opportunity model is constructed using the adaptive clustering for the similar traffic. In a mobile machine network, the next hop node is determined by the node clustering principle. A LEACH clustering protocol enables communication between cluster heads and base stations based on single-hop and multihop cluster nodes. In order to perform adaptive clustering of mobile network nodes based on network partitioning and scheduling of clusters, genetic algorithms are used. The proposed approach can be applied to the IIoT systems in places where adaptive clustering is required to optimize energy consumption, to reduce latency rates, and to enhance the throughput of mobile networks. The experimental findings suggest that the proposed adaptive strategy is capable of optimizing energy consumption rates, reducing network latency, and boosting efficiency by increasing throughput in mobile opportunistic networks.

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Section: Introductionmentioning

confidence: 99%

Adaptive Clustering Algorithm for IIoT Based Mobile Opportunistic Networks

Zhao

Akter

2022

Security and Communication Networks

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“…This is additionally confirmed by Table 1 that shows a list of frequent usages of GA specifically for the FS task, grouped by the type of modification made to the SGA. Decomposition of solution population [34], [38] - [35], [14] Fitness function aggregation [8], [48], [13], [19], [33] [29], [51] [12]…”

Section: Genetic Algorithm For Feature Selectionmentioning

confidence: 99%

An empirical study of the crossover operator in a genetic algorithm used as a wrapper for feature selection

Dudjak

2023

Preprint

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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

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“…( 2016 ), Hassan and Mohammed ( 2020 ), Mlakar et al. ( 2017 ), Karthiga and Mangai ( 2019 ) proposed expression recognition systems with EAs and feature selection. Mistry et al.…”

Section: Binary Metaheuristic Algorithms In Applicationsmentioning

confidence: 99%

“…( 2016 ) mGA & PSO Facial Emotion Recognition Fast Medium Medium Hassan and Mohammed ( 2020 ) Binary CSO Facial Emotion Recognition Medium Medium Low Mlakar et al. ( 2017 ) MO DE Facial Emotion Recognition Medium Medium Low Karthiga and Mangai ( 2019 ) MO GA Facial Emotion Recognition Medium Medium Medium Chantar et al. ( 2020 ) Binary GWO Text Classification Low Medium Medium Thiyagarajan and Shanthi ( 2019 ) MO AFSA Text Classification Medium Medium High Labani et al.…”

Section: Binary Metaheuristic Algorithms In Applicationsmentioning

confidence: 99%

A survey on binary metaheuristic algorithms and their engineering applications

Pan

Snåšel

et al. 2022

Artif Intell Rev

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This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications. It is seen that transfer function is the main binary coding of metaheuristic algorithms, which usually adopts Sigmoid function. Among the contributions presented, there were different implementations and applications of metaheuristic algorithms, or the study of engineering applications by different objective functions such as the single- and multi-objective problems of feature selection, scheduling, layout and engineering structure optimization. The article identifies current troubles and challenges by the conducted review, and discusses that novel binary algorithm, transfer function, benchmark function, time-consuming problem and application integration are need to be resolved in future.

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Feature Selection Using Multi-Objective Modified Genetic Algorithm in Multimodal Biometric System

Cited by 15 publications

References 25 publications

Adaptive Clustering Algorithm for IIoT Based Mobile Opportunistic Networks

Adaptive Clustering Algorithm for IIoT Based Mobile Opportunistic Networks

An empirical study of the crossover operator in a genetic algorithm used as a wrapper for feature selection

A survey on binary metaheuristic algorithms and their engineering applications

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