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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Wang, Lin Orchard, Jeff Yang, Bo Abraham, Ajith Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Peoples R China Univ Waterloo David R Cheriton Sch Comp Sci Waterloo ON Canada VSB Tech Univ Ostrava Fac Elect Engn & Comp Sci Dept Comp Sci Ostrava Czech Republic VSB Tech Univ Ostrava Natl Supercomp Ctr IT4Innovat Ostrava Czech Republic MIR Labs Sci Network Innovat & Res Excellence Auburn WA USA

ISBN: (纸本)9781509018970

In gene expression programming, diversity can be reduced during evolution, sometimes resulting in premature convergence because of non-coding regions, leading to substantial reproduction of repeated individuals. In order to increase the diversity of the population and to avoid premature convergence, we propose a new diversity preservation tournament operator, adopting a tree-based similarity measurement and global probability weights. Furthermore, the proposed tournament operator is embedded into a hybrid evolution architecture to search for a parsimonious model for the firing pattern of grid cells, neurons in the mammalian brain involved in navigation. Experimental results demonstrate that the proposed diversity preservation tournament improves the performance of gene expression programming for evolving a model for grid-cell data.

关键词： Diversity Preservation Tournament Grid Cell gene expression programming Neuroscience

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Multi-Temporal Satellite Image Analysis Using gene expression programming 2

Multi-Temporal Satellite Image Analysis Using Gene Expressio...

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2nd International Conference on Soft Computing for Problem Solving (SocProS)

作者： Senthilnath, J. Omkar, S. N. Mani, V. Vanjare, Ashoka Diwakar, P. G. Indian Inst Sci Dept Aerosp Engn Bangalore 560012 Karnataka India Earth Observat Syst ISRO Head Quarters Bangalore Karnataka India

ISBN: (纸本)9788132216025;9788132216018

This paper discusses an approach for river mapping and flood evaluation to aid multi-temporal time series analysis of satellite images utilizing pixel spectral information for image classification and region-based segmentation to extract water covered region. Analysis of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images is applied in two stages: before flood and during flood. For these images the extraction of water region utilizes spectral information for image classification and spatial information for image segmentation. Multi-temporal MODIS images from "normal" (non-flood) and flood time-periods are processed in two steps. In the first step, image classifiers such as artificial neural networks and gene expression programming to separate the image pixels into water and non-water groups based on their spectral features. The classified image is then segmented using spatial features of the water pixels to remove the misclassified water region. From the results obtained, we evaluate the performance of the method and conclude that the use of image classification and region-based segmentation is an accurate and reliable for the extraction of water-covered region.

关键词： MODIS satellite image gene expression programming Artificial neural network

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MGEPM: Specific composition style melody generation based on music gene expression programming 10

MGEPM: Specific composition style melody generation based on...

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10th International Conference on Big Data and Information Analytics

作者： Zhou, Xuanfei Luo, Weicheng Yuan, Yutong Tang, Boya Qiao, Linbo Wang, Chen Chen, Xingxuan Changsha Normal Univ Presch Coll Changsha Peoples R China Shantou Univ Coll Engn Shantou Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha Peoples R China Ludong Univ Sch Arts Yantai Peoples R China Natl Univ Def Technol Sch Comp Changsha Peoples R China

ISBN: (纸本)9798350354638;9798350354621

This study proposes a music gene expression programming method to achieve music melody feature learning of a specific singer's compositional style. The user specifies the song format, and the program automatically generates and evaluates songs. A mapping mechanism between 16-bit octal numbers and musical scores is proposed to realize the evolution of musical scores in units of measures. In the melody generation stage, the modification operations that are generally performed by the composer in the process of composing music are integrated with the evolutionary operations of gene expression programming to create a music gene expression programming operation. In the music melody evaluation stage, a long short-term memory neural network evaluation model is established and trained using the main verse and chorus of the music as well as the spectrum of the entire song. The neural network evaluates the resulting song fitness value, providing feedback for music evolution. In contrast to melody generated via other methods, comparative experiments show that the melody generated by this research method has an obvious compositional style of a specific composer.

关键词： automatic melody generation music-style learning gene expression programming long short-term memory evolutionary algorithm deep learning

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Classifying Remote Sensing Image Using gene expression programming Algorithms

Classifying Remote Sensing Image Using Gene Expression Progr...

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9th International Conference on Natural Computation (ICNC)

作者： Liu, Mengwei Zeng, Guanghong Yuan, Guohui Pei, Yabo Yang, Zili Real Estate Surveying & Mapping Inst Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781467347143

gene expression programming (GEP) is a new algorithm of evolutionary computation, which possesses much more powerful abilities of parallel computation and global search to resolve complex problems. This paper constructs the GEP classification model and carries out experiments with Landsat Thematic Mapper(TM) image of Dongguan city in 1997. The result shows that the GEP classification model has good classification accuracy and demonstrates effectiveness in remote sensing image classification.

关键词： Evolution Algorithm gene expression programming RS Image Classification

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Research on Image Fractal Compression Coding Algorithm Based on gene expression programming 17

Research on Image Fractal Compression Coding Algorithm Based...

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17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES)

作者： Li, Wenjing Pan, Qiuxia Lu, Jianbo Li, Songzhao Guangxi Teachers Educ Univ Sch Logist Management & Engn Nanning Peoples R China Guangxi Teachers Educ Univ Coll Comp & Informat Engn Nanning Peoples R China

ISBN: (纸本)9781538674451

A kind of image fractal compression coding algorithm based on gene expression programming is put forward to deal with such problems as low image compression rate and slow image fractal compression coding speed. Firstly, the sub-block division is conducted on the original image and its relative gradient image with Image Adaptive Quadtree Segmentation methods according to the characteristics of self-similarity of original image and its relative gradient image;Secondly, the solution process of relative gradient image fractal compression coding and the action mechanism of gene expression programming in compression encoding is analyzed theoretically. The expression methods of gene and chromosome of image fractal compression coding. Experimental results show that if the relative gradient and gene expression programming are applied in image fractal compression coding in a combined way, the global optimization ability will be stronger, the speed of searching optimal solution will be about 2 times faster than the genetic algorithm and the image compression rate will be higher.

关键词： Adaptive segmentation gene expression programming Image compression Fractal encoding Intelligent Algorithm

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Function Mining based on gene expression programming and Particle Swarm Optimization

Function Mining based on Gene Expression Programming and Par...

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2nd IEEE International Conference on Computer Science and Information Technology

作者： Li, Taiyong Wu, Jiang Dong, Tiangang He, Ting Southwestern Univ Finance & Econ Sch Econ Informat Engn Chengdu 610074 Peoples R China Southwestern Univ Finance & Econ Res Ctr China Payment Syst Chengdu 610074 Peoples R China Sichuan Univ Sch Comp Sci Chengdu 610065 Peoples R China Chengdu Univ Traditional Chinese Med Coll Pharmaceut Sci Chengdu 611130 Peoples R China

ISBN: (纸本)9781424445189

gene expression programming (GEP) is a powerful tool widely used in function mining. However, it is difficult for GEP to generate appropriate numeric constants for function mining. In this paper, a novel approach of creating numeric constants, GEPPSO, was proposed, which embedded Particle Swarm Optimization (PSO) into GEP. In the approach, the evolutionary process was divided into 2 phases: in the first phase, GEP focused on optimizing the structure of function expression, and in the second one, PSO focused on optimizing the constant parameters. The experimental results on function mining problems show that the performance of GEPPSO is better than that of the existing GEP Random Numerical Constants algorithm (GEP-RNC).

关键词： evolutionary algorithm function mining gene expression programming particle swarm optimization

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Numerical study of the bridge pier scour using gene expression programming

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JOURNAL OF APPLIED WATER ENGINEERING AND RESEARCH 2019年第4期7卷 287-294页

作者： Saleh, Layla Ali Mohammed Majeed, Sumayah Amal Al-din Alnasrawi, Fatin Abd el-kadhium M. Univ Kerbala Coll Engn Civil Dept Kerbala Iraq

Local scour is considered a natural phenomenon resulting from the erosive effect of flowing water. In this study, gene expression programming (GEP) was applied to predict the local scour depth around bridge piers for both live-bed and clear-water conditions. Input variables that were considered as an effective parameter on scour depth modeling include properties of sediment size, pier geometry, and upstream flow conditions. The training and testing stages of GEP models were carried out using laboratory data sets collected from different literature. Furthermore, these data sets were used to develop nonlinear regression equations to predict scour depth for both flow conditions. Testing results indicated that the GEP models predicted the scour depth with lower error and higher accuracy than those performed using new-regression models and previous empirical equations (RMSE = 0.09, MAE = 0.07, and R-2 = 0.9) for live-bed GEP model and (RMSE = 0.12, MAE = 0.08, and R-2 = 0.92) for clear-water GEP model.

关键词： Clear-water gene expression programming empirical equations live-bed scouring process

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A gene expression programming Environment for Fatigue Modeling of Composite Materials

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6th Hellenic Conference on Artificial Intelligence

作者： Antoniou, Maria A. Georgopoulos, Efstratios F. Theofilatos, Konstantinos A. Vassilopoulos, Anastasios P. Likothanassis, Spiridon D. Univ Patras Dept Comp Engn & Informat Pattern Recognit Lab Patras 26500 Greece Inst Educ Technol Kalamata Kalamata 24100 Greece Ecole Polytech Fed Lausanne CCLab CH-1015 Lausanne Switzerland

ISBN: (纸本)9783642128417

In the current paper is presented the application of a gene expression programming Environment in modeling the fatigue behavior of composite materials. The environment was developed using the JAVA programming language, and is an implementation of a variation of gene expression programming. gene expression programming (GEP) is a new evolutionary algorithm that evolves computer programs (they can take many forms: mathematical expressions, neural networks, decision trees, polynomial constructs, logical expressions, and so on). The computer programs of GEP, irrespective of their complexity, are all encoded in linear chromosomes. Then the linear chromosomes are expressed or translated into expression trees (branched structures). Thus, in GEP, the genotype (the linear chromosomes) and the phenotype (the expression trees) are different entities (both structurally and functionally). This is the main difference between GEP and classical tree based genetic programming techniques. In order to evaluate the performance of the presented environment, we tested it in fatigue modeling of composite materials.

关键词： gene expression programming genetic programming Evolutionary Algorithms System Modeling Fatigue Modeling

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High energy physics data analysis with gene expression programming

High energy physics data analysis with gene expression progr...

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Nuclear Science Symposium/Medical Imaging Conference

作者： Teodorescu, Liliana Brunel Univ London England

ISBN: (纸本)0780392213

gene expression programming is a new evolutionary algorithm that overcomes many limitations of the more established genetic Algorithms and genetic programming. Its first application to high energy physics data analysis is presented. The algorithm was successfully used for event selection on samples with both low and high background level. The signal/background classification accuracy was over 90% in all cases.

关键词： evolutionary algorithms gene expression programming event selection

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Evolutionary NAS with gene expression programming of Cellular Encoding

Evolutionary NAS with Gene Expression Programming of Cellula...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Broni-Bediako, Cliford Murata, Yuki Mormille, Luiz H. B. Atsumi, Masayasu Soka Univ Grad Sch Sci & Engn Hachioji Tokyo Japan

ISBN: (纸本)9781728125473

The renaissance of neural architecture search (NAS) has seen classical methods such as genetic algorithms (GA) and genetic programming (GP) being exploited for convolutional neural network (CNN) architectures. While recent work have achieved promising performance on visual perception tasks, the direct encoding scheme of both GA and GP has functional complexity deficiency and does not scale well on large architectures like CNN. To address this, we present a new generative encoding scheme-symbolic linear generative encoding (SLGE)-simple, yet a powerful scheme which embeds local graph transformations in chromosomes of linear fixed-length string to develop CNN architectures of variant shapes and sizes via an evolutionary process of gene expression programming. In experiments, the effectiveness of SLGE is shown in discovering architectures that improve the performance of the stale-of-the-art handcrafted CNN architectures on CIFAR-10 and CIFAR-100 image classification tasks;and achieves a competitive classification error rate with the existing NAS methods using fewer GPU resources.

关键词： neural architecture search convolutional neural network gene expression programming cellular encoding

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