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20th Chinese Control and Decision Conference

作者： Chen, Yuehui Jia, Guangfeng Xiu, Liming Univ Jinan Sch Informat Sci & Engn Jinan 250022 Peoples R China

ISBN: (纸本)9781424417339

Automatic designing of both architecture and parameters of an artificial neural network is an important problem. This paper introduces a new approach for designing artificial neural networks using multi expression programming (MEP-NN). The approach employs the multi expression programming to evolve the architecture and the parameters encoded in the neural network simultaneously. Based on the predefined instruction sets, a MEP-NN model can be created and evolved. This framework allows input variables selection, over-layer connections and different activation functions for the various nodes involved. The performance and effectiveness of the proposed method are evaluated using stock market forecasting problems and compared with the related methods.

关键词： Artificial Neural Network multi expression programming Feature Selection Stock Market Forecasting

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Energy-based numerical models for assessment of soil liquefaction

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Geoscience Frontiers 2012年第4期3卷 541-555页

作者： Amir Hossein Alavi Amir Hossein Gandomi School of Civil Engineering lran University of Science and TechnologyTehranIran College of Civil Engineering Tafresh UniversityTafreshlran

This study presents promising variants of genetic programming （GP）, namely linear genetic programming （LGP） and multi expression programming （MEP） to evaluate the liquefaction resistance of san- dy soils. Generalized LGP and MEP-based relationships were developed between the strain energy density required to trigger liquefaction （capacity energy） and the factors affecting the liquefaction characteristics of sands. The correlations were established based on well established and widely dispersed experimental results obtained from the literature. To verify the applicability of the derived models, they were employed to estimate the capacity energy values of parts of the test results that were not included in the analysis. The external validation of the models was verified using statistical criteria recommended by researchers. Sensitivity and parametric analyses were performed for further verification of the correlations. The results indicate that the proposed correlations are effectively capable of capturing the liquefaction resistance of a number of sandy soils. The developed correlations provide a significantly better prediction performance than the models found in the literature. Furthermore, the best LGP and MEP models perform superior than the optimal traditional GP model. The verification phases confirm the efficiency of the derived correlations for their general application to the assessment of the strain energy at the onset of liquefaction.

关键词： Soil liquefaction Capacity energy Linear genetic programming multi expression programming Sand Formulation

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ME-CGP: multi expression Cartesian Genetic programming

ME-CGP: Multi Expression Cartesian Genetic Programming

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2010 IEEE World Congress on Computational Intelligence

作者： Cattani, Phil T. Johnson, Colin G. Univ Kent Sch Comp Canterbury Kent England

ISBN: (纸本)9781424481262

Cartesian Genetic programming (CGP) is a form of Genetic programming that uses directed graphs to represent programs. In this paper we propose a way of structuring a CGP algorithm to make use of the multiple phenotypes which are implicitly encoded in a genome string. We show that this leads to a large increase in efficiency compared with standard CGP where genomes are translated into only one phenotype. We call this method multi expression CGP (ME-CGP), based on Mihai Oltean's work on multi expression programming using linear GP.

关键词： CGP ME-CGP directed graphs genetic algorithms graph theory multi expression cartesian genetic programming multi expression programming multiple phenotypes

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Permanent deformation analysis of asphalt mixtures using soft computing techniques

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第5期38卷 6081-6100页

作者： Mirzahosseini, Mohammad Reza Aghaeifar, Alireza Alavi, Amir Hossein Gandomi, Amir Hossein Seyednour, Reza Univ Social Welf & Rehabil Sci Dept Res Tehran Iran Kansas State Univ Dept Civil Engn Manhattan KS 66506 USA Iran Univ Sci & Technol Sch Railway Engn Tehran Iran Iran Univ Sci & Technol Sch Civil Engn Tehran Iran Tafresh Univ Coll Civil Engn Tafresh Iran

This study presents two branches of soft computing techniques, namely multi expression programming (MEP) and multilayer perceptron (MLP) of artificial neural networks for the evaluation of rutting potential of dense asphalt-aggregate mixtures. Constitutive MEP and MLP-based relationships were obtained correlating the flow number of Marshall specimens to the coarse and fine aggregate contents, percentage of bitumen, percentage of voids in mineral aggregate. Marshall stability, and Marshall flow. Different correlations were developed using different combinations of the influencing parameters. The comprehensive experimental database used for the development of the correlations was established upon a series of uni-axial dynamic creep tests conducted in this study. Relative importance values of various predictor variables of the models were calculated to determine the significance of each of the variables to the flow number. A multiple least squares regression (MLSR) analysis was performed to benchmark the MEP and MLP models. For more verification, a subsequent parametric study was also carried out and the trends of the results were confirmed with the experimental study results and those of previous studies. The observed agreement between the predicted and measured flow number values validates the efficiency of the proposed correlations for the assessment of the rutting potential of asphalt mixtures. The MEP-based straightforward formulas are much more practical for the engineering applications compared with the complicated equations provided by MLP. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Asphalt pavements Rutting multi expression programming Artificial neural network Marshall mix design Formulation

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Numerical modeling of stress-strain behavior of sand under cyclic loading

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ENGINEERING GEOLOGY 2010年第1-2期116卷 53-72页

作者： Shahnazari, Habib Dehnavi, Yasser Alavi, Amir Hossein Iran Univ Sci & Technol Sch Civil Engn Tehran Iran

Analysis of soil response to cyclic loading is a complex problem due to the participation of a large number of loading conditions and physical properties. In this study, a promising variant of genetic programming (GP), namely multi expression programming (MEP) is utilized to model stress-strain behavior of sands subjected to large amplitude regular cyclic loading. Generalized MEP-based formulations are derived to simulate hysteresis strain-stress curves. To construct the hysteresis curves, functional relationships are obtained between maximum shear stress at different loading cycles and several influencing parameters. New constitutive correlations are further developed for the assessment of damping ratio and shear modulus. The proposed correlations are established based on several drained strain-controlled cyclic torsional simple shear tests performed on samples of Toyoura sand. The correlations take into account the effects of initial effective confining pressure, initial relative density, initial anisotropic stress state, strain amplitude, and shear history. A subsequent parametric study is carried out and the obtained trends are confirmed with the experimental study results. For more validity verification, the MEP-based correlations are applied to simulate the stress-strain curves of a part of the laboratory results beyond the training data domain. The proposed models are effectively capable of capturing the complex hysteresis behavior of sandy soils under cyclic loading conditions. The derived correlations are particularly valuable for providing an analysis tool accessible to practicing engineers. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Cyclic stress strain relationship Formulation multi expression programming Damping ratio Shear modulus

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A data mining approach to compressive strength of CFRP-confined concrete cylinders

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STRUCTURAL ENGINEERING AND MECHANICS 2010年第6期36卷 759-783页

作者： Mousavi, S. M. Alavi, A. H. Gandomi, A. H. Esmaeili, M. Arab Gandomi, M. Iran Univ Sci & Technol Sch Civil Engn Tehran Iran

In this paper, compressive strength of carbon fiber reinforced polymer (CFRP) confined concrete cylinders is formulated using a hybrid method coupling genetic programming (GP) and simulated annealing (SA), called GP/SA, and a robust variant of GP, namely multi expression programming (MEP). Straightforward GP/SA and MEP-based prediction equations are derived for the compressive strength of CFRP-wrapped concrete cylinders. The models are constructed using two sets of predictor variables. The first set comprises diameter of concrete cylinder, unconfined concrete strength, tensile strength of CFRP laminate, and total thickness of CFRP layer. The most widely used parameters of unconfined concrete strength and ultimate confinement pressure are included in the second set. The models are developed based on the experimental results obtained from the literature. To verify the applicability of the proposed models, they are employed to estimate the compressive strength of parts of test results that were not included in the modeling process. A sensitivity analysis is carried out to determine the contributions of the parameters affecting the compressive strength. For more verification, a parametric study is carried out and the trends of the results are confirmed via some previous studies. The GP/SA and MEP models are able to predict the ultimate compressive strength with an acceptable level of accuracy. The proposed models perform superior than several CFRP confinement models found in the literature. The derived models are particularly valuable for pre-design purposes.

关键词： CFRP-confined concrete compressive strength genetic programming simulated annealing multi expression programming formulation

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A Unified Fitness Calculation Method for Automatic Modeling Algorithms

A Unified Fitness Calculation Method for Automatic Modeling ...

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Jiang, Dazhi Wang, Zhifei Sun, Haojun Du, Yulin Univ Shantou Dept Comp Sci Shantou Guangdong Peoples R China

ISBN: (纸本)9781424467129

As a branch of Evolutionary Computation (EC), Gene expression programming (GEP) and multi expression programming (MEP) have been applied widely in Automatic Modeling. Based on the Gene Reading and Computing Machine, a unified and improved method named Improved Reading and Computing Machine (IGRCM) is presented to calculate the fitness of chromosome for two linear automatic modeling algorithms, which are GEP and MEP. The experimental results indicate that the unified IGRCM could save the CPU running time effectively.

关键词： Automatic Modeling Evolutionary Computation Gene expression programming multi expression programming

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GENETIC programming WITH LINEAR REPRESENTATION: A SURVEY

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INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2009年第2期18卷 197-238页

作者： Oltean, Mihai Grosan, Crina Diosan, Laura Mihaila, Cristina Univ Babes Bolyai Dept Comp Sci Fac Math & Comp Sci Cluj Napoca 400084 Romania

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.

关键词： Genetic programming linear genetic programming gene expression programming multi expression programming grammatical evolution Cartesian genetic programming stack-based genetic programmig

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Inference of Differential Equations for Modeling Chemical Reactions

Inference of Differential Equations for Modeling Chemical Re...

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6th International Symposium on Neural Networks

作者： Yang, Bin Chen, Yuchui Meng, Qingfang Univ Jinan Computat Intelligence Lab Sch Informat Sci & Engn Jinan 250022 Peoples R China

ISBN: (纸本)9783642015069

This paper presents an evolutionary method for identifying a system of ordinary differential equations (ODEs) from the observed time series data. The structure of ODE is inferred by the multi expression programming (MEP) and the ODE's parameters are optimized by Using particle swarm optimization (PSO). The experimental results on chemical reaction modeling problems show effectiveness of the proposed method.

关键词： multi expression programming Ordinary differential equations Particle swarm optimization Chemical reaction

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MENN Method Applications for Stock Market Forecasting

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5th International Symposium on Neural Networks

作者： Jia, Guangfeng Chen, Yuehui Wu, Peng Univ Jinan Sch Informat Sci & Engn Jinan 250022 Peoples R China

ISBN: (纸本)9783540877318

A new approach for forecasting stock index based oil multi expression Neural Network (MENN) is proposed in this paper. The approach employs the multi expression programming (MEP) to evolve the architecture of the MENN and the particle swarm optimization (PSO) to optimize the parameters encoded in the MENN. This framework allows input variables selection, over-layer connections for the various nodes involved. The performance and effectiveness of the proposed method are. evaluated using stock market forecasting problems and compared with the related methods.

关键词： multi expression programming Artificial Neural Network Stock Market Forecasting

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