Gene Expression Programming: A Survey [Review Article]

Zhong, Jinghui; Feng, Liang; Ong, Yew-Soon

doi:10.1109/mci.2017.2708618

Cited by 131 publications

(34 citation statements)

References 106 publications

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“…Like other techniques such as genetic algorithms (GA) or genetic programming (GP), GEP operates using the fundamental principles of genetic evolution. Generally, GEP has been applied to five categories of problems: symbolic regression, classification, automatic model design, combinatorial optimization, and real parameter optimization [21]. GEP leverages the strengths of genetic-based algorithms while retaining simple genetic operations and faster convergence than GP approaches in complex optimization problems [22].…”

Section: Previous Workmentioning

confidence: 99%

Biometric Identification from Human Aesthetic Preferences

Sieu

Gavrilova

2020

Sensors

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In recent years, human–machine interactions encompass many avenues of life, ranging from personal communications to professional activities. This trend has allowed for person identification based on behavior rather than physical traits to emerge as a growing research domain, which spans areas such as online education, e-commerce, e-communication, and biometric security. The expression of opinions is an example of online behavior that is commonly shared through the liking of online images. Visual aesthetic is a behavioral biometric that involves using a person’s sense of fondness for images. The identification of individuals using their visual aesthetic values as discriminatory features is an emerging domain of research. This paper introduces a novel method for aesthetic feature dimensionality reduction using gene expression programming. The proposed system is capable of using a tree-based genetic approach for feature recombination. Reducing feature dimensionality improves classifier accuracy, reduces computation runtime, and minimizes required storage. The results obtained on a dataset of 200 Flickr users evaluating 40,000 images demonstrate a 95% accuracy of identity recognition based solely on users’ aesthetic preferences.

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Section: Previous Workmentioning

confidence: 99%

Biometric Identification from Human Aesthetic Preferences

Sieu

Gavrilova

2020

Sensors

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“…The initial population is constructed by a blind random search of the space defined by the problem. The formula obtained by each individual in a population is composed of a random combination of functions (mathematical operators, such as square and addition) and terminals (variables and constants [40,41]) and these mathematical approximations are actually computer programs with a tree structure. Selection of the structure of an analytical expression is a problem of empirical model building.…”

Section: Metamodel Building By Genetic Programming (Gp)mentioning

confidence: 99%

Metaheuristic Approaches to Solve a Complex Aircraft Performance Optimization Problem

2019

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The increasing demands for travelling comfort and reduction of carbon dioxide emissions have been considered substantially in the stage of conceptual aircraft design. However, the design of a modern aircraft is a multidisciplinary process, which requires the coordination of information from several specific disciplines, such as structures, aerodynamics, control, etc. To address this problem with adequate accuracy, the multidisciplinary analysis and optimization (MAO) method is usually applied as a systematic and robust approach to solve such complex design issues arising from industries. Since MAO method is tedious and computationally expensive, genetic programming (GP)-based metamodeling techniques incorporating MAO are proposed as an effective approach to minimize the wing stiffness of a large aircraft subject to aerodynamic, aeroelastic and stability constraints in the conceptual design phase. Based on the linear small-disturbance theory, the state-space equation is employed for stability analysis. In the process of multidisciplinary analysis, aeroelastic response simulations are performed using Nastran. To construct metamodels representing the responses of the interests with high accuracy as well as less computational burden, optimal Latin hypercube design of experiments (DoE) is applied to determine the optimized distribution of sampling points. Following that, parametric optimization is carried out on metamodels to obtain the optimal wing geometry shape, elastic axis positions and stiffness distribution, and then the solution is verified by finite element simulations. Finally, the superiority of the GP-based metamodel technique over genetic algorithm is demonstrated by multidisciplinary design optimization of a representative beam-frame wing structure in terms of accuracy and efficiency. The results also show that GP metamodel-based strategy for solving MAO problems can provide valuable insights to tailoring parameters for the effective design of a large aircraft in the conceptual phase.

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“…Unlike other population-based algorithms that must be implemented with a given machine learning classifier, GP is capable of completing both feature selection and classification independently owing to its tree structure. By converting the tree structure of GP into a string structure, Gene Expression Programming (GEP) (Zhong et al, 2017), a variant of GP, enjoys the same benefit as GP of independent classification ability with additional power of controlling bloat issue by restricted string length (Ferreira, 2002). With the automatic construction capability, GEP-based methods have emerged to show high effectiveness on symbolic regression (Cheng and Zhong, 2018;Huang et al, 2018;Zhong et al, 2018b), time series prediction (Zuo et al, 2004), knowledge discovery (Zhong et al, 2014), and etc.…”

Section: Introductionmentioning

confidence: 99%

A Preliminary Study of Knowledge Transfer in Multi-Classification Using Gene Expression Programming

Wei

Zhong

2020

Front. Neurosci.

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Gene Expression Programming (GEP), a variant of Genetic Programming (GP), is a well established technique for automatic generation of computer programs. Due to the flexible representation, GEP has long been concerned as a classification algorithm for various applications. Whereas, GEP cannot be extended to multi-classification directly, and thus is only capable of treating an M-classification task as M separate binary classifications without considering the interrelationship among classes. Consequently, GEP-based multi-classifier may suffer from output conflict of various class labels, and the underlying conflict can probably lead to the degraded performance in multi-classification. This paper employs evolutionary multitasking optimization paradigm in an existing GEP-based multi-classification framework, so as to alleviate the output conflict of each separate binary GEP classifier. Therefore, several knowledge transfer strategies are implemented to enable the interation among the population of each separate binary task. Experimental results on 10 high-dimensional datasets indicate that knowledge transfer among separate binary classifiers can enhance multi-classification performance within the same computational budget.

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Gene Expression Programming: A Survey [Review Article]

Cited by 131 publications

References 106 publications

Biometric Identification from Human Aesthetic Preferences

Biometric Identification from Human Aesthetic Preferences

Metaheuristic Approaches to Solve a Complex Aircraft Performance Optimization Problem

A Preliminary Study of Knowledge Transfer in Multi-Classification Using Gene Expression Programming

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