Relevant bibliographies by topics / Genetic Expression Programming

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Genetic Expression Programming'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Genetic Expression Programming"

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FERREIRA, CÂNDIDA. "GENETIC REPRESENTATION AND GENETIC NEUTRALITY IN GENE EXPRESSION PROGRAMMING." Advances in Complex Systems 05, no. 04 (December 2002): 389–408. http://dx.doi.org/10.1142/s0219525902000626.

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The neutral theory of molecular evolution states that the accumulation of neutral mutations in the genome is fundamental for evolution to occur. The genetic representation of gene expression programming, an artificial genotype/phenotype system, not only allows the existence of non-coding regions in the genome where neutral mutations can accumulate but also allows the controlled manipulation of both the number and the extent of these non-coding regions. Therefore, gene expression programming is an ideal artificial system where the neutral theory of evolution can be tested in order to gain some insights into the workings of artificial evolutionary systems. The results presented in this work show beyond any doubt that the existence of neutral regions in the genome is fundamental for evolution to occur efficiently.
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Chen, Long Bin, and Pei He. "Multi-Subexpression Programming." Applied Mechanics and Materials 411-414 (September 2013): 2067–73. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.2067.

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Gene Expression Programming is a new and adaptive brand evolution algorithm which is developed on the basis of genetic algorithm. In recent years, Multi-Expression Programming which is proposed in the genetic programming is a linear structure coding scheme,its main feature is a chromosome contains multiple expressions. The idea of MEP is introduced into the GEP in this paper, so a single GEP gene contains multiple solutions to solve the problem.The new algorithm analyzes each gene in the GEP to extract relational subexpressions, then fitness evaluate certain subexpressions to choose the best fitness as individuals fitness, and carry on related genetic manipulation. Finally, the improved algorithm experiment with GEP and MEP, compare their mining the same functions ability,record average fitness value and success rate. The experiment results show that the improved algorithm has better evolutionary efficiency.
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Hosseini, Maryamsadat, Rouzbeh Shad, and Samsung Lim. "Landslide Susceptibility Mapping using Genetic Expression Programming." IOP Conference Series: Earth and Environmental Science 767, no. 1 (May 1, 2021): 012042. http://dx.doi.org/10.1088/1755-1315/767/1/012042.

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ASLAN, Behzat, and Fevzi Önen. "APPLICATION OF GENETIC EXPRESSION PROGRAMMING IN URBAN DRINKING WATER." Middle East Journal of Technic 2, no. 2 (December 30, 2017): 143–55. http://dx.doi.org/10.23884/mejt.2017.2.2.01.

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OLTEAN, MIHAI, CRINA GROŞAN, LAURA DIOŞAN, and CRISTINA MIHĂILĂ. "GENETIC PROGRAMMING WITH LINEAR REPRESENTATION: A SURVEY." International Journal on Artificial Intelligence Tools 18, no. 02 (April 2009): 197–238. http://dx.doi.org/10.1142/s0218213009000111.

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Genetic Programming (GP) is an automated method for creating computer programs starting from a high-level description of the problem to be solved. Many variants of GP have been proposed in the recent years. In this paper we are reviewing the main GP variants with linear representation. Namely, Linear Genetic Programming, Gene Expression Programming, Multi Expression Programming, Grammatical Evolution, Cartesian Genetic Programming and Stack-Based Genetic Programming. A complete description is provided for each method. The set of applications where the methods have been applied and several Internet sites with more information about them are also given.
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Abraham, Ajith, and Crina Grosan. "Decision Support Systems Using Ensemble Genetic Programming." Journal of Information & Knowledge Management 05, no. 04 (December 2006): 303–13. http://dx.doi.org/10.1142/s0219649206001566.

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This paper proposes a decision support system for tactical air combat environment using a combination of unsupervised learning for clustering the data and an ensemble of three well-known genetic programming techniques to classify the different decision regions accurately. The genetic programming techniques used are: Linear Genetic programming (LGP), Multi-Expression Programming (MEP) and Gene Expression Programming (GEP). The clustered data are used as the inputs to the genetic programming algorithms. Some simulation results demonstrating the difference of these techniques are also performed. Test results reveal that the proposed ensemble method performed better than the individual GP approaches and that the method is efficient.
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CAZENAVE, TRISTAN. "MONTE-CARLO EXPRESSION DISCOVERY." International Journal on Artificial Intelligence Tools 22, no. 01 (February 2013): 1250035. http://dx.doi.org/10.1142/s0218213012500352.

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Monte-Carlo Tree Search is a general search algorithm that gives good results in games. Genetic Programming evaluates and combines trees to discover expressions that maximize a given fitness function. In this paper Monte-Carlo Tree Search is used to generate expressions that are evaluated in the same way as in Genetic Programming. Monte-Carlo Tree Search is transformed in order to search expression trees rather than lists of moves. We compare Nested Monte-Carlo Search to UCT (Upper Confidence Bounds for Trees) for various problems. Monte-Carlo Tree Search achieves state of the art results on multiple benchmark problems. The proposed approach is simple to program, does not suffer from expression growth, has a natural restart strategy to avoid local optima and is extremely easy to parallelize.
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Faradonbeh, Roohollah Shirani, Danial Jahed Armaghani, Masoud Monjezi, and Edy Tonnizam Mohamad. "Genetic programming and gene expression programming for flyrock assessment due to mine blasting." International Journal of Rock Mechanics and Mining Sciences 88 (October 2016): 254–64. http://dx.doi.org/10.1016/j.ijrmms.2016.07.028.

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Gao, Xin Wen, Ben Bo Guan, and Xing Jian Guan. "Study on the Optimize Strategies of Gene Expression Programming." Applied Mechanics and Materials 432 (September 2013): 565–70. http://dx.doi.org/10.4028/www.scientific.net/amm.432.565.

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The purpose of this paper is to improve the efficiency of the Gene Expression Programming (GEP) algorithm. The GEP algorithm is an evolutionary computation. It inherits the characteristics of Genetic Algorithm and Genetic Programming. Through its own characteristics, the GEP algorithm can get the optimal solution of the complicated problem. So, the GEP algorithm has achieved good results in many areas. However, there are also some inevitable drawbacks about the GEP algorithm itself. This paper proposes 5 deficiencies aspects of the GEP algorithm (expression meaning, fitness calculation, local convergence, variable selection, genetic operations, selection of genetic operation rates), and gives the corresponding solutions.
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Sharifi, S., M. Sterling, and D. W. Knight. "Prediction of end-depth ratio in open channels using genetic programming." Journal of Hydroinformatics 13, no. 1 (March 18, 2010): 36–48. http://dx.doi.org/10.2166/hydro.2010.087.

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In this paper, genetic programming (GP) is used as an effective model induction tool to solve a classic problem in open channel flow: the free overfall. By applying GP to experimental data of circular channels with a flat bed and employing a model selection procedure, a reliable expression in the form of is found for calculating the critical depth (hc) and end-depth ratio (EDR). Further effort is made to verify the applicability and superiority of this expression for channels with other cross sections. This global expression not only outperforms other expressions in estimating the critical depth, it is also dimensionally correct (unlike some other applications of GP) and can be used for channels with any cross-section and any flow regime.
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Dissertations / Theses on the topic "Genetic Expression Programming"

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Cattani, Philip Thomas. "Extending Cartesian genetic programming : multi-expression genomes and applications in image processing and classification." Thesis, University of Kent, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.655651.

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Genetic Programming (GP) is an Evolutionary Computation technique. Genetic Programming refers to a programming strategy where an artificial population of individuals represent solutions to a problem in the form of programs, and where an iterative process of selection and reproduction is used in order to evolve increasingly better solutions. This strategy is inspired by Charles Darwin's theory of evolution through the mechanism of natural selection. Genetic Programming makes use of computational procedures analogous to some of the same biological processes which occur in natural evolution, namely, crossover, mutation, selection, and reproduction. Cartesian Genetic Programming (CGP) is a form of Genetic Programming that uses directed graphs to represent programs. It is called 'Cartesian', because this representation uses a grid of nodes that are addressed using a Cartesian co-ordinate system. This stands in contrast to GP systems which typically use a tree-based system to represent programs. In this thesis, we will show how it is possible to enhance and extend Cartesian Genetic Programming in two ways. Firstly, we show how CGP can be made to evolve programs which make use of image manipulation functions in order to create image manipulation programs. These programs can then be applied to image classification tasks as well as other image manipulation tasks such as segmentation, the creation of image filters, and transforming an input image in to a target image. Secondly, we show how the efficiency - the time it takes to solve a problem - of a CGP program can sometimes be increased by reinterpreting the semantics of a CGP genome string. We do this by applying Multi-Expression Programming to CGP.
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González, David Muñoz. "Discovering unknown equations that describe large data sets using genetic programming techniques." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2639.

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FIR filters are widely used nowadays, with applications from MP3 players, Hi-Fi systems, digital TVs, etc. to communication systems like wireless communication. They are implemented in DSPs and there are several trade-offs that make important to have an exact as possible estimation of the required filter order.

In order to find a better estimation of the filter order than the existing ones, genetic expression programming (GEP) is used. GEP is a Genetic Algorithm that can be used in function finding. It is implemented in a commercial application which, after the appropriate input file and settings have been provided, performs the evolution of the individuals in the input file so that a good solution is found. The thesis is the first one in this new research line.

The aim has been not only reaching the desired estimation but also pave the way for further investigations.

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POSTERNAK, DAN. "INFERENCE OF THE ANALYTICAL EXPRESSION FROM AN OPTIMAL INVESTMENT BOUNDARY FOR AN ASSET THAT FOLLOWS THE REVERSION MEAN PROCESS THROUGH GENETIC PROGRAMMING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2004. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=5797@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
Esta Pesquisa tem por objetivo utilizar a Regressão Simbólica por Programação Genética para encontrar uma equação analítica para a fronteira de exercício ótima (ou curva de gatilho) de uma opção sobre um ativo do qual o preço tem um comportamento simulado pelo processo estocástico conhecido como processo de reversão à média (PRM). Para o cálculo do valor de uma opção desde de sua aquisição até sua maturação, normalmente faz-se o uso do cálculo da fronteira de exercício ótimo. Esta curva separa ao longo do tempo a decisão de exercer ou não a opção. Sabendo-se que já existem soluções analíticas para calcular a fronteira de exercício ótimo quando o preço do ativo segue um Movimento Geométrico Browniano, e que tal solução genérica ainda não foi encontrada para o PRM, neste trabalho, foi proposto o uso da Programação Genética (PG) para encontrar tal solução analítica. A Programação Genética utilizou um conjunto de amostras de curvas de exercício ótimo parametrizadas segundo a variação da volatilidade e da taxa de juros livre de risco, para encontrar uma função analítica para a fronteira de exercício ótima, obtendo-se resultados satisfatórios.
This research intends on to use the Symbolic Regression by Genetic Programming to find an analytical equation that represents an Optimal Exercise Boundary for an option of an asset having its price behavior simulated by a stochastic process known as Mean Reversion Process (MRP). To calculate an option value since its acquisition until its maturity, normally is used to calculate the Optimal Exercise Boundary. This frontier separates along the time the decision to exercise the option or not. Knowing there already are analytical solutions used to calculate the Optimal Exercise Boundary when the asset price follows the Geometric Brownian Motion, and such general solution was not found yet to MRP, in this work, it was proposed the use of Genetic Programming to find such analytical solution. The Genetic Programming used an amount of samples from optimal exercise curves parameterized according the change in the volatility and risk free interest rate, to find an analytical function that represents Optimal Exercise Boundary, achieving satisfactory results.
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Siau, Nor Zainah. "A teachable semi-automatic web information extraction system based on evolved regular expression patterns." Thesis, Loughborough University, 2014. https://dspace.lboro.ac.uk/2134/14687.

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This thesis explores Web Information Extraction (WIE) and how it has been used in decision making and to support businesses in their daily operations. The research focuses on a WIE system based on Genetic Programming (GP) with an extensible model to enhance the automatic extractor. This uses a human as a teacher to identify and extract relevant information from the semi-structured HTML webpages. Regular expressions, which have been chosen as the pattern matching tool, are automatically generated based on the training data to provide an improved grammar and lexicon. This particularly benefits the GP system which may need to extend its lexicon in the presence of new tokens in the web pages. These tokens allow the GP method to produce new extraction patterns for new requirements.
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Isele, Robert [Verfasser], and Christian [Akademischer Betreuer] Bizer. "Learning Expressive Linkage Rules for Entity Matching using Genetic Programming / Robert Isele. Betreuer: Christian Bizer." Mannheim : Universitätsbibliothek Mannheim, 2013. http://d-nb.info/1038671809/34.

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Xhemali, Daniela. "Automated retrieval and extraction of training course information from unstructured web pages." Thesis, Loughborough University, 2010. https://dspace.lboro.ac.uk/2134/7022.

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Web Information Extraction (WIE) is the discipline dealing with the discovery, processing and extraction of specific pieces of information from semi-structured or unstructured web pages. The World Wide Web comprises billions of web pages and there is much need for systems that will locate, extract and integrate the acquired knowledge into organisations practices. There are some commercial, automated web extraction software packages, however their success comes from heavily involving their users in the process of finding the relevant web pages, preparing the system to recognise items of interest on these pages and manually dealing with the evaluation and storage of the extracted results. This research has explored WIE, specifically with regard to the automation of the extraction and validation of online training information. The work also includes research and development in the area of automated Web Information Retrieval (WIR), more specifically in Web Searching (or Crawling) and Web Classification. Different technologies were considered, however after much consideration, Naïve Bayes Networks were chosen as the most suitable for the development of the classification system. The extraction part of the system used Genetic Programming (GP) for the generation of web extraction solutions. Specifically, GP was used to evolve Regular Expressions, which were then used to extract specific training course information from the web such as: course names, prices, dates and locations. The experimental results indicate that all three aspects of this research perform very well, with the Web Crawler outperforming existing crawling systems, the Web Classifier performing with an accuracy of over 95% and a precision of over 98%, and the Web Extractor achieving an accuracy of over 94% for the extraction of course titles and an accuracy of just under 67% for the extraction of other course attributes such as dates, prices and locations. Furthermore, the overall work is of great significance to the sponsoring company, as it simplifies and improves the existing time-consuming, labour-intensive and error-prone manual techniques, as will be discussed in this thesis. The prototype developed in this research works in the background and requires very little, often no, human assistance.
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Siqueira, Flavia Ramos de. "Restrição no consumo de sódio durante a gestação é responsável pelo baixo peso ao nascimento e pela resistência à insulina da prole na idade adulta: estudo do mecanismo epigenético por metilação do DNA." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/5/5148/tde-13082014-142638/.

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Sabe-se que algumas alterações nutricionais maternas durante o período perinatal estão associadas com doenças metabólicas na vida adulta das proles, tais como diabetes melito tipo 2, resistência à insulina, obesidade e hipertensão arterial. O período da gestação em que estas alterações nutricionais influenciam a prole na idade adulta ainda não está elucidado. Modificações epigenéticas têm sido propostas como mecanismos responsáveis por estas desordens metabólicas. Ratas Wistar de doze semanas de idade foram alimentadas com dieta com conteúdo baixo (HO - 0,15% NaCl) ou normal (NR - 1,3% NaCl) de sódio desde o primeiro dia de gestação até o nascimento da prole ou HO durante a primeira (HO10) ou segunda (HO20) metade da gestação. O peso corpóreo e a ingestão de água e ração foram avaliados semanalmente durante a gestação. Teste de tolerância à insulina (ITT) e à glicose (GTT) e HOMA-IR foram realizados nas proles adultas. Expressão gênica por qRT-PCR e metilação do DNA na região promotora dos genes foram mapeadas utilizando tratamento com bissulfito de sódio e avaliadas por pirosequenciamento. O ganho de peso materno foi menor no HO e HO20 na terceira semana de gestação em comparação com NR e HO10. O peso ao nascimento da prole foi menor em machos e fêmeas dos grupos HO e HO20 em relação ao NR e HO10. O HOMA-IR foi maior nos machos com 12 semanas de idade do grupo HO em comparação com NR e com 20 semanas de idade do grupo HO10 em comparação com NR e HO20. Nas fêmeas com 12 semanas de idade o HOMA-IR foi maior no HO10 comparado com HO. Os níveis de insulina no soro foram maiores tanto nos machos com 20 semanas de idade do grupo HO10 comparado com NR quanto nas fêmeas com 12 semanas de idade do grupo HO10 comparado com HO. A área sob a curva do GTT indicou intolerância à glicose nos machos do grupo HO. A porcentagem de metilação das ilhas CpG no promotor dos genes de Igf1, Igf1r, Ins1, Ins2 e Insr no fígado de machos e fêmeas neonatais e no fígado, tecido adiposo branco e músculo em machos com 20 semanas de idade foi influenciada pela baixa ingestão de sal durante a gestação. Nenhuma destas alterações foi identificada nas fêmeas com 20 semanas de idade. Em conclusão, a baixa ingestão de sal na segunda metade da gestação é responsável pelo baixo peso ao nascimento em ambos os sexos. A intolerância à glicose observada na prole adulta ocorreu somente se a dieta hipossódica é dada durante a gestação inteira. Por outro lado, a resistência à insulina em resposta ao consumo de dieta hipossódica durante a gestação está relacionada com o momento em que ocorre este insulto e com o envelhecimento da prole. Também foi observado que alterações na metilação do promotor do gene Igf1 está correlacionado com o baixo peso ao nascimento em resposta a ingestão de dieta hipossódica durante a gestação
It is known that some maternal nutritional alterations during pregnancy are associated with metabolic disorders in adult offspring, such as insulin resistance, type 2 diabetes mellitus, obesity and arterial hypertension. The period of pregnancy in which these nutritional alterations influence adult offspring remains uncertain. Epigenetic changes are proposed to underlie these metabolic disorders. Twelve-week-old female Wistar rats were fed a low-salt (LS - 0.15% NaCl) or normal-salt (NS - 1.3% NaCl) diet since the first day of gestation until delivery or LS during the first (LS10) or second (LS20) half of gestation. Body weight, food and water intake were weekly evaluated during gestation. Blood glucose, insulin (ITT) and glucose (GTT) tolerance tests, HOMA-IR were performed in adult offspring. Gene expression and DNA methylation were mapped using bisulfite treatment evaluated by pyrosequencing in the male and female neonates and adult offspring. Weight gain was lower in LS and LS20 dams than in NS and LS10 dams in the third week of pregnancy. Birth weights were lower in male and female LS20 and LS rats compared with NS and LS10 neonates. HOMA-IR was higher in 12-week-old LS males compared with NS and in 20-week-old male LS10 rats compared with NS and LS20 rats. In 12-week-old LS10 females, HOMA-IR was higher than in LS. Serum insulin levels were higher in 20 week-old LS10 male compared with NS rats and in 12-week-old LS10 female compared to LS rats. The area under the curve of GTT indicated glucose intolerance in 12- and 20-week-old LS male. Methylation of CpG islands of the Insr, Igf1, Igf1r, Ins1 and Ins2 genes in liver in neonates male and female offspring and liver, white adipose tissue and muscle in 20-week-old male offspring were influenced by low-salt intake during pregnancy. None of these alterations was identified in 20-week-old females. In conclusion, low-salt diet consumption in the second half of pregnancy can result in low birth weights in the males and females offspring. Glucose intolerance observed in adult offspring occurred only if low salt intake was given throughout pregnancy. However, insulin resistance in response to low salt intake during pregnancy is related to the time at which this insult occurs and to the age of the offspring. Alterations in the DNA methylation of Igf1 were observed to be correlated with low birth weight in response to low salt feeding during pregnancy
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Liu, Bo-Heng, and 劉伯恆. "Digital Music Classification Using Genetic Expression Programming." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/66711263875247180376.

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碩士
元智大學
資訊管理學系
96
There have been many algorithm proposed to solve music classification problems. The composition of music context is complicated, and the music genre is defined by musical perception. Lacking of qualifications to determinate music genres makes music classification more difficult. In this paper, method based on genetic expression programming to classify Midi music files was proposed. This method uses statistical information of Midi file features to classify Midi music genres, and builds models and classification rules. The result can be use for music recommendation or classification systems.
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Ho, Ya-Wei, and 何亞威. "GPS GDOP Approximation Using Genetic Expression Programming." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/28140862985483734446.

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碩士
國立高雄大學
電機工程學系碩士班
98
Global Positioning System (GPS) has been used extensively in various fields. One key to success of using GPS is the positioning accuracy. Geometric Dilution of Precision (GDOP) is an indicator showing how well the constellation of GPS satellites is organized geometrically. Traditional methods for the calculation of GDOP need to solve the measurement equations with complicated matrix transformation and inversion. GDOP can also be viewed as a regression problem from satellite signals. Previous study employs black-boxed machine learning methods for solving this problem. However, the structure of the regression models obtained from these methods is unknown so that they can not be analyzed extensively. This study employs the technique of genetic expression programming (GEP) for the regression of GPS GDOP. The regression models obtained from GEP have visible structures and can be modified in GPS application software. Several new input types for regression are defined. The experimental results show that GEP can generate precise models for GSP GDOP than other regression methods.
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Scott, Kristen Marie. "A multiple expression alignment framework for genetic programming." Master's thesis, 2018. http://hdl.handle.net/10362/40749.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
Alignment in the error space is a recent idea to exploit semantic awareness in genetic programming. In a previous contribution, the concepts of optimally aligned and optimally coplanar individuals were introduced, and it was shown that given optimally aligned, or optimally coplanar, individuals, it is possible to construct a globally optimal solution analytically. Consequently, genetic programming methods, aimed at searching for optimally aligned, or optimally coplanar, individuals were introduced. This paper critically discusses those methods, analyzing their major limitations and introduces a new genetic programming system aimed at overcoming those limitations. The presented experimental results, conducted on five real-life symbolic regression problems, show that the proposed algorithms’ outperform not only the existing methods based on the concept of alignment in the error space, but also geometric semantic genetic programming and standard genetic programming.
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Books on the topic "Genetic Expression Programming"

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The nonlinear workbook: Chaos, fractals, cellular automata, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, fuzzy logic with C++, Java and symbolic C++ programs. 6th ed. Hackensack, New Jersey: World Scientific, 2015.

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The nonlinear workbook: Chaos, fractals, cellular automata, neural networks, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, Fuzzy logic with C++, Java and SymbolicC++ programs. 5th ed. New Jersey: World Scientific, 2011.

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The nonlinear workbook: Chaos, fractals, cellular automata, neural networks, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, Fuzzy logic with C++, Java and SymbolicC++ programs. 3rd ed. Hackensack, NJ: World Scientific, 2005.

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The nonlinear workbook: Chaos, fractals, cellular automata, neural networks, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, Fuzzy logic with C++, Java and SymbolicC++ programs. 4th ed. New Jersey: World Scientific, 2008.

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The nonlinear workbook: Chaos, fractals, cellular automata, neural networks, genetic algorithms, gene expression programming, support vector machine, wavelets, hidden Markov models, Fuzzy logic with C++, Java and SymbolicC++ programs. 5th ed. New Jersey: World Scientific, 2011.

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Nakov, Svetlin. Fundamentals of Computer Programming with C#: The Bulgarian C# Book. Sofia, Bulgaria: Svetlin Nakov, 2013.

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Ferreira, Cândida. Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence). 2nd ed. Springer, 2006.

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Nonlinear Workbook: Chaos, Fractals, Cellular Automata, Neural Networks, Genetic Algorithms, Gene Expression Programming, Wavelets, Fuzzy Logic - With C++, Java and SymbolicC++ Programs. 2nd ed. World Scientific Pub Co Inc, 2003.

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Nonlinear Workbook: Chaos, Fractals, Cellular Automata, Neural Networks, Genetic Algorithms, Gene Expression Programming, Wavelets, Fuzzy Logic - With C++, Java and SymbolicC++ Programs. 2nd ed. World Scientific Publishing Company, 2003.

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Book chapters on the topic "Genetic Expression Programming"

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Korns, Michael F. "Abstract Expression Grammar Symbolic Regression." In Genetic Programming Theory and Practice VIII, 109–28. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7747-2_7.

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Flores, Juan J., and Mario Graff. "System Identification Using Genetic Programming and Gene Expression Programming." In Computer and Information Sciences - ISCIS 2005, 503–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11569596_53.

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Zahiri, A., A. A. Dehghani, and H. Md Azamathulla. "Application of Gene-Expression Programming in Hydraulic Engineering." In Handbook of Genetic Programming Applications, 71–97. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20883-1_4.

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Driscoll, Joseph A., Bill Worzel, and Duncan MacLean. "Classification of Gene Expression Data with Genetic Programming." In Genetic Programming Theory and Practice, 25–42. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-8983-3_3.

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Vanneschi, Leonardo, Kristen Scott, and Mauro Castelli. "A Multiple Expression Alignment Framework for Genetic Programming." In Lecture Notes in Computer Science, 166–83. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77553-1_11.

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Guogis, Evaldas, and Alfonsas Misevičius. "Comparison of Genetic Programming, Grammatical Evolution and Gene Expression Programming Techniques." In Communications in Computer and Information Science, 182–93. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11958-8_15.

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Bhanu, Bir, Jiangang Yu, Xuejun Tan, and Yingqiang Lin. "Feature Synthesis Using Genetic Programming for Face Expression Recognition." In Genetic and Evolutionary Computation – GECCO 2004, 896–907. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24855-2_103.

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Serrato Paniagua, Ramiro, Juan J. Flores Romero, and Carlos A. Coello Coello. "A Genetic Representation for Dynamic System Qualitative Models on Genetic Programming: A Gene Expression Programming Approach." In MICAI 2007: Advances in Artificial Intelligence, 30–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-76631-5_4.

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Kumoyama, Daichi, Yoshiko Hanada, and Keiko Ono. "A New Probabilistic Tree Expression for Probabilistic Model Building Genetic Programming." In Computational Science/Intelligence and Applied Informatics, 121–32. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25225-0_9.

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Dorado, Julian, Juan R. Rabuñal, Antonino Santos, Alejandro Pazos, and Daniel Rivero. "Automatic Recurrent and Feed-Forward ANN Rule and Expression Extraction with Genetic Programming." In Parallel Problem Solving from Nature — PPSN VII, 485–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45712-7_47.

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Conference papers on the topic "Genetic Expression Programming"

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Zhu, Ming-Fang, Chang-Jie Tang, Shao-Jie Qiao, Shu-Cheng Dai, and Yu Chen. "Genetic Neutrality in Naive Gene Expression Programming." In 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2008. http://dx.doi.org/10.1109/wicom.2008.2602.

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Cattani, Phil T., and Colin G. Johnson. "ME-CGP: Multi Expression Cartesian Genetic Programming." In 2010 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2010. http://dx.doi.org/10.1109/cec.2010.5586478.

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Ma, Jun, Fenghui Gao, Shuangrong Liu, and Lin Wang. "Linear-dependent multi-interpretation neuro-encoded expression programming." In GECCO '21: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449726.3459498.

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Xu, Congwen, Qiang Lu, Jake Luo, and Zhiguang Wang. "Adversarial bandit gene expression programming for symbolic regression." In GECCO '21: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449726.3459499.

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Mo, Haifang, Jiangqing Wang, Jun Qin, and Lishan Kang. "Function Finding Based on Gene Expression Programming." In 2008 Second International Conference on Genetic and Evolutionary Computing (WGEC). IEEE, 2008. http://dx.doi.org/10.1109/wgec.2008.85.

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Lu, Qiang, Shuo Zhou, Fan Tao, and Zhiguang Wang. "Space partition based gene expression programming for symbolic regression." In GECCO '19: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3319619.3322075.

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Bi, Ying, Bing Xue, and Mengjie Zhang. "Genetic Programming-Based Feature Learning for Facial Expression Classification." In 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2020. http://dx.doi.org/10.1109/cec48606.2020.9185491.

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Omkar, S. N., Nikhil Ramaswamy, J. Senthilnath, S. Bharath, and N. S. Anuradha. "Gene Expression Programming-Fuzzy Logic Method for Crop Type Classification." In 2012 Sixth International Conference on Genetic and Evolutionary Computing (ICGEC). IEEE, 2012. http://dx.doi.org/10.1109/icgec.2012.97.

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Hung, Lung-Hsuan, and Chih-Hung Wu. "Load prediction of virtual machine servers using genetic expression programming." In 2013 International Conference on Fuzzy Theory and Its Applications (iFUZZY). IEEE, 2013. http://dx.doi.org/10.1109/ifuzzy.2013.6825473.

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Chen, Yunliang, Jianzhong Huang, Changsheng Xie, Yunliang Chen, and Juan Yang. "Using Uniform-Design Genetic Expression Programming for chaotic time series prediction." In 2010 3rd International Congress on Image and Signal Processing (CISP). IEEE, 2010. http://dx.doi.org/10.1109/cisp.2010.5646785.

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