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International Conference on Information Science, Automation and Material System

作者： Li, Yanmei Wang, Jingmin N China Elect Power Univ Sch Business & Adm Baoding 071003 Peoples R China

ISBN: (纸本)9783037850817

Applying the improved gene expression programming arithmetic to optimization plan, the convergence rate and precision of the model can be improved, which can be used to load forecasting. Preprocessing the load sample data, and applying the flexible skills of the improved gene expression programming arithmetic,the paper forecasts the whole point load of future short-term to see the same point load sequence of different work-day as sample. Through a case analysis,the improved gene expression programming arithmetic has been proved to have more efficiency and faster convergence rate than optimization methods.

关键词： gene expression programming mutation operator load forecasting

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Mining Class Contrast Functions by gene expression programming

Mining Class Contrast Functions by Gene Expression Programmi...

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5th International Conference on Advanced Data Mining and Applications

作者： Duan, Lei Tang, Changjie Tang, Liang Zhang, Tianqing Zuo, Jie Sichuan Univ Sch Comp Sci Chengdu 610065 Sichuan Peoples R China

ISBN: (纸本)9783642033476

Finding functions whose accuracies change significantly between two classes is an interesting work. In this paper, this kind of functions is defined as class contrast functions. As gene expression programming (GEP) can discover essential relations from data and express them mathematically, it is desirable to apply GEP to mining such class contrast functions from data. The main contributions of this paper include: (1) proposing a new data mining task - class contrast function mining, (2) designing a GEP based method to find class contrast functions, (3) presenting several strategies for finding multiple class contrast functions in data, (4) giving an extensive performance study on both synthetic and real world datasets. The experimental results show that the proposed methods are effective. Several class contrast functions are discovered from the real world datasets. Some potential works on class contrast function mining are discussed based on the experimental results.

关键词： gene expression programming Contrast Mining Data Mining

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Robust gene expression programming

Robust Gene Expression Programming

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Conference of the Complex Adaptive Systems on Responding to Continuous Global Change in Systems Needs

作者： Ryan, Noah Hibler, David Christopher Newport Univ PCSE Dept Newport News VA 23606 USA

genetic/evolutionary methods are frequently used to deal with complex adaptive systems. The classic example is a genetic Algorithm. A genetic Algorithm uses a simple linear representation for possible solutions to a problem. This is usually a bit vector. Unfortunately, the natural representation for many problems is a tree structure. In order to deal with these types of problems many evolutionary methods make use of tree structures directly. gene expression programming is a new, popular evolutionary technique that deals with these types of problems by using a linear representation for trees. In this paper we present and evaluate Robust gene expression programming (RGEP). This technique is a simplification of gene expression programming that is equally efficient and powerful. The underlying representation of a solution to a problem in RGEP is a bit vector as in genetic Algorithms. It has fewer and simpler operators than those of gene expression programming. We describe the basic technique, discuss its advantages over related methods, and evaluate its effectiveness on example problems. (C) 2011 Published by Elsevier B.V.

关键词： Evolutionary Computation gene expression programming

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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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Application of gene expression programming to predict the compressive strength of quaternary-blended concrete

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Asian Journal of Civil Engineering 2023年第5期24卷 1351-1364页

作者： Raheel, Muhammad Iqbal, Mudassir Khan, Rawid Alam, Muhammad Azab, Marc Eldin, Sayed M. Department of Civil Engineering University of Engineering and Technology Mardan Pakistan Department of Civil Engineering University of Engineering and Technology Peshawar Pakistan College of Engineering and Technology American University of the Middle East Egaila 54200 Kuwait Center of Research Faculty of Engineering Future University in Egypt New Cairo Egypt

The potential impact of global warming is influencing life on planet earth. Besides other sources, production of cement is one of the major contributors of greenhouse gas emissions. Considering this, a study was undertaken to understand the influence of potential waste materials, which can replace cement and at the same time, make it economical and environment friendly. For this purpose, the influence of a quaternary blend of binders i.e., cement + 3 different pozzolanic materials on the compressive strength of concrete. Different variables such as the quantity of different binders, fine and coarse aggregate, water, superplasticizer and age of the samples were considered to study their influence on the compressive strength on the quaternary-blended concrete using gene expression programming (GEP). The number of chromosomes, genes and, the head size of GEP model was varied to study their influence on the predicted values of compressive strength. The performance of these different GEP models was also assessed using R², RMSE and comparison of regression slopes. It was observed that the model with the number of chromosomes = 150, head size = 9 and the number of genes = 3, resulted in an optimum GEP model as apparent from its high R² = 0.801 in the TR phase, and 0.800 in the TS phase, respectively. The regression slope analysis revealed that the forecasted values show good agreement with the actual values as evident from their higher R² values (≥ 0.80). Similarly, monotonicity analysis for the best performing model M6 revealed that the addition of pozzolanic materials enhanced the compressive strength of quaternary-blended concrete. It was also observed that the compressive strength of quaternary-blended concrete increased sharply within the first 28 days of casting, thus validating the practical mechanics of concrete strength gaining process. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Compressive strength gene expression programming Machine learning Parametric and sensitivity analyses Quaternary-blended concrete

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Multi-expression Based gene expression programming

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Chinese Intelligent Automation Conference (CIAC)

作者： Deng, Wei He, Pei Huang, Zhi Changsha Univ Sci & Technol Sch Comp & Commun Engn Changsha 410114 Hunan Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China

ISBN: (纸本)9783642384660;9783642384653

Among the variants of GP, GEP stands out for its simplicity of encoding method and MEP catches our attention for its multi-expression capability. In this paper, a novel GP variant-MGEP (Multi-expression based gene expression programming) is proposed to combine these two approaches. The new method preserves the GEP structure, however unlike the traditional GEP, its genes, like those of MEP, can be disassembled into many expressions. Therefore in MGEP, the traditional GEP gene can contain multiple solutions for a problem. The experimental result shows the MGEP is more effective than the traditional GEP and MEP in solving problems.

关键词： genetic programming gene expression programming Multi expression programming

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Using gene expression programming to develop a combined runoff estimate model from conventional rainfall-runoff model outputs 18

Using gene expression programming to develop a combined runo...

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Combined IMACS World Congress/Modelling and Simulation Society-of-Australia-and-New-Zealand (MSSANZ)/18th Biennial Conference on Modelling and Simulation

作者： Fernando, D. A. K. Shamseldin, A. Y. Abrahart, R. J. Unitec New Zealand Dept Civil Engn Auckland New Zealand

ISBN: (纸本)9780975840078

In previous studies, artificial neural networks have been used to develop a model that combines simulated river flows from several individual rainfall-runoff models e. g. Shamseldin et al., 1997;Abrahart & See, 2002;Shamseldin, O'Connor, & Nasr, 2007. The combined runoff estimate model was found to perform better than the individual models in most of the cases. However, no attempts have been made to explain the inner workings of the combined models or the drivers for their success. The research presented in this study investigates the use of gene expression programming (GEP) to develop a combination rainfall-runoff model through the process of symbolic regression. One of the additional advantages of this approach over the neural combination method is the model's ability to represent itself in the form of mathematical expressions. The GEP model is developed using the daily simulated river flows of four other rainfall runoff models for the Chu catchment which is located in Vietnam. The four models are the linear perturbation model (LPM), the linearly varying gain factor model (LVGFM), the probability-distributed interacting storage capacity (PDISC) model, and the soil moisture accounting and routing (SMAR) models. In this paper, geneXproTools 4.0, a powerful soft computing software package, is used to develop the combined model. The program provides transparent modeling solutions in the sense that it provides the users with the mathematical equation describing the combined model. The results reveal that combination using symbolic regression is successful and that a superior combined model can be developed using outputs from other individual models. The structure of the combined model is also investigated in this study. The results show that the combined model is dominated by input information from the PDISC model forming the baseline estimate, to which different permutations and combinations of the remaining inputs from the other models are added. This research, limit

关键词： gene expression programming Rainfall-runoff models Combination models Over-fitting

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Distributed reflectance model mining of leaf nitrogen content by using gene expression programming 5

Distributed reflectance model mining of leaf nitrogen conten...

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5th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)

作者： Yang, Lechan Li, Wenjuan Qin, Zhihao Deng, Song Tu, Lili Nanjing Univ Int Inst Earth Syst Sci Nanjing 210093 Jiangsu Peoples R China Chinese Acad Agr Sci Inst Agr Resources & Reg Planning Beijing 100081 Peoples R China Nanjing Univ Post & Telecommun Inst Adv Technol Nanjing 210003 Jiangsu Peoples R China

ISBN: (纸本)9781509023509

Estimating dynamically for leaf nitrogen concentration is an important subject in the studies on crop monitoring. The traditional regression methods including linear regression, partial least squares regression, support vector machine regression and random forest regression depended on a priori knowledge and many subjective factors. Moreover, these methods have high time complexity and low computational efficiency for complex and high-dimensional hyperspectral data. In order to better find reflectance model of LNC for complex and high-dimensional hyperspectral data, this paper presents distributed reflectance model mining of leaf nitrogen content by using gene expression programming (DRMMLNC-GEP) which combined with GEP and grid service. The comparative results show that the DRMMLNC-GEP outperforms all other algorithms on the average time-consumption, value of R-square and prediction accuracy. Meanwhile, experimental results also show that with the increasing of datasets size, DRMMLNC-GEP demonstrates good speed-up ratio and scale-up ratio too.

关键词： reflectance model leaf nitrogen content gene expression programming nonlinear least squares

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Integration of Reaction Kinetics Theory and gene expression programming to Infer Reaction Mechanism

Integration of Reaction Kinetics Theory and Gene Expression ...

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作者： Jason R. White University of Connecticut

学位级别：博士

Mechanistic mathematical models of biological systems have been used to describe biological phenomena, including human disease, in the hope that one day these models may be used to better understand diseases, as well as to develop and optimize therapeutic strategies. Evolutionary algorithms, such as genetic programming, may be used to symbolically regress mathematical models describing chemical and biochemical species for which kinetic data are available. However, current evolutionary algorithms are restricted to the formulation of simple or approximate models due to the computational cost of evolving mechanistic models for more complex systems. It was hypothesized that chemical reaction kinetic theory could be used to sufficiently reduce the model search space for an evolutionary algorithm such that it would be possible to infer mechanistic mathematical models of complex biological interactions. An evolutionary algorithm capable of formulating mass action kinetic models of biological systems from time series data sets was developed for a system of n-species using heuristics from chemical reaction kinetic theory and a gene expression programming (GEP) based approach. The resulting algorithm was then successfully validated on a general model of viral dynamics that accounted for six pathways relating the change in viral template, viral genome, and viral structural protein concentrations over time. The algorithm was applied to generate cohort-specific models of HIV dynamics from a clinical data set. HIV-1 infection models were defined as sets of two ordinary differential equations describing the change in CD4+ T-cell and HIV-1 concentrations over time. The evolved models were used to generate hypotheses regarding treatment effectiveness and the potential for viral rebound in three cohorts of HIV-1 positive individuals receiving different Highly Active Antiretroviral Therapy (HAART) regimens. It was hypothesized by the algorithm that HAART was effective in stopping HIV-

关键词： network inference reaction kinetics genetic programming gene expression programming viral dynamics human immunodeficiency virus systems biology

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Study on music emotion cognition model based on applying the improved gene expression programming

Study on music emotion cognition model based on applying the...

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2nd International Workshop on Digital Media and its Application in Museum and Heritage

作者： Yang, Cheng Sun, Shouqian Zhang, Kejun Liu, Tao Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Peoples R China

ISBN: (纸本)9780769530659

This paper proposes a music emotion cognition model by applying the improved genetic algorithm with dynamic mutation operator. Firstly, a Hevner emotion ring representing the music emotion space is presented by utilizing psychological fuzzy measure on words in music psychology. Secondly, the music emotion vector is introduced based on semantic similarity relation in Computing with Words. Then the mapping from high dimensional feature space of music to emotion space is built by using the genetic algorithm which can mine the emotion expressions. Finally the comparisons with some famous learning algorithms such as BP neural network, regression method with least square, show that the proposed method is effective for music emotion cognition.

关键词： emotion cognition fuzzy logic gene expression programming dynamic mutation operator

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