A survey of genetic algorithm-based face recognition

Dai, Fengzhi; Kushida, Naoki; Shang, Liqiang; Sugisaka, Masanori

doi:10.1007/s10015-011-0941-9

Cited by 7 publications

(8 citation statements)

References 5 publications

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“…Therefore, genetic algorithm is utilized to select the best features and reduce the dimensionality of the training dataset. The GA is a wellestablished and popular algorithm with recognition applications as it yields good optimization for "noisy" environments [7][8][9]. This is what distinguishes GA in dealing with the extraction of features in the Arabic words, which has a lot of noise represented in the great overlap between the words due to the presence of many of the similar letters.…”

Section: E Feature Selectionmentioning

confidence: 99%

“…Genetic Algorithms (GAs) are stochastic optimization methods based on the mechanics of natural evolution and natural genetics [8] [9]. They work with a population of individuals, each representing a practical solution in the research space.…”

Section: E Feature Selectionmentioning

confidence: 99%

“…Classical optimization methods should use some information. The GA is a well-established and popular algorithm with recognition applications as it yields good optimization for "noisy" environments [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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An Enhanced Offline Printed Arabic OCR Model Based on Bio-Inspired Fuzzy Classifier

Darwish

Elzoghaly

2020

IEEE Access

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In the recent few years, there was a concentrated search on Arabic Optical Character Recognition (OCR), especially the recognition of scanned, offline, machine-printed documents. However, Arabic OCR consequences are dissatisfying and are still a developed research area. Finding the best feature extraction techniques and selecting an appropriate classification algorithm lead to supreme recognition accuracy and low computational overhead. This paper presents a new Arabic OCR model by integrating both of Genetic Algorithm (GA) and the Fuzzy K-Nearest Neighbor classifier (F-KNN) in a unified framework to enhance the identification accuracy. GA is utilized as a feature selection algorithm that has better convergence and spread of solutions with candid variation preservation mechanism. The F-KNN algorithm is more appropriate to classify ambiguous or uncertain data objects in the sense that every object belongs to all classes with different degrees of membership. The suggested model semantically fuses bio-inspired based feature vectors with fuzzy KNN classifier to build accurate membership function for each class. Experimental results compared to other approaches revealed the effectiveness of the suggested model and demonstrated that the feature selection approach increased the identification accuracy process.

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Section: E Feature Selectionmentioning

confidence: 99%

Section: E Feature Selectionmentioning

confidence: 99%

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An Enhanced Offline Printed Arabic OCR Model Based on Bio-Inspired Fuzzy Classifier

Darwish

Elzoghaly

2020

IEEE Access

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“…Another operation, named mutation, produces a sporadic and random change through the bits of binary strings. The mutation also has a straight analogy from nature that can play the role of reproducing lost genetic material [3][4][5][6][7][8][9][10].…”

Section: Definition Of Gas and Gpsmentioning

confidence: 99%

A Survey of Genetic Programming and Its Applications

Ahvanooey¹,

Li²,

Wu³

et al. 2019

KSII TIIS

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Genetic Programming (GP) is an intelligence technique whereby computer programs are encoded as a set of genes which are evolved utilizing a Genetic Algorithm (GA). In other words, the GP employs novel optimization techniques to modify computer programs; imitating the way humans develop programs by progressively rewriting them for solving problems automatically. Trial programs are frequently altered in the search for obtaining superior solutions due to the base is GA. These are evolutionary search techniques inspired by biological evolution such as mutation, reproduction, natural selection, recombination, and survival of the fittest. The power of GAs is being represented by an advancing range of applications; vector processing, quantum computing, VLSI circuit layout, and so on. But one of the most significant uses of GAs is the automatic generation of programs. Technically, the GP solves problems automatically without having to tell the computer specifically how to process it. To meet this requirement, the GP utilizes GAs to a "population" of trial programs, traditionally encoded in memory as tree-structures. Trial programs are estimated using a "fitness function" and the suited solutions picked for re-evaluation and modification such that this sequence is replicated until a "correct" program is generated. GP has represented its power by modifying a simple program for categorizing news stories, executing optical character recognition, medical signal filters, and for target identification, etc. This paper reviews existing literature regarding the GPs and their applications in different scientific fields and aims to provide an easy understanding of various types of GPs for beginners.

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“…Bayesian regularization algorithm (BRA) holds strong generalization ability and global optimization ability in searching for the model parameters by gradual approximation in training process [16]. Genetic algorithm (GA) effectively avoids premature convergence and local optimization in modeling process [21,22]. erefore, it is important to adopt GA to improve the accuracy of BP-ANN model for flexible mechanism reliability analysis.…”

Section: Introductionmentioning

confidence: 99%

Transient Reliability Evaluation Approach of Flexible Mechanism with GA-Extremum Neural Network

Zhao

Fei

et al. 2020

Mathematical Problems in Engineering

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Efficient analytical model directly enhances the reliability evaluation of flexible mechanism under operation. In this paper, genetic algorithm-based extremum neural network (GA-ENN) is developed as reliability model by introducing the thoughts of extremum and genetic algorithm (GA) into artificial neural network to address the key problems comprising transient response and modeling precision in the dynamic reliability analysis of flexible mechanism in a time domain. The thought of extremum is adopted to simplify transient response process as one extremum value to the difficulty of dynamic reliability analysis induced by transient process response, and the GA is applied to find the optimal model parameters of reliability model. The dynamic reliability analysis of two-link flexible robot manipulator (TFRM) (a typical flexible mechanism) was implemented based on the GA-ENN method, regarding the input random variables of material density, elastic modulus, section sizes of components, and the output response of components’ deformations. From the analysis, the comprehensive reliability of the TFRM is 0.951 when the allowable deformation is 1.8 × 10−2 m. Besides, the maximum deformations of the two components follow the normal distributions with the means of 1.45 × 10−2 m and 1.69 × 10−2 m and the standard variances of 6.77 × 10−4 m and 4.08 × 10−4 m, respectively. Through the comparison of methods, it is illustrated that the developed GA-ENN improves the simulation efficiency and modeling accuracy by overcoming the problems of transient response and model parameter optimization in the dynamic reliability analysis of TFRM.

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A survey of genetic algorithm-based face recognition

Cited by 7 publications

References 5 publications

An Enhanced Offline Printed Arabic OCR Model Based on Bio-Inspired Fuzzy Classifier

An Enhanced Offline Printed Arabic OCR Model Based on Bio-Inspired Fuzzy Classifier

A Survey of Genetic Programming and Its Applications

Transient Reliability Evaluation Approach of Flexible Mechanism with GA-Extremum Neural Network

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