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Robust genetic programming-based detection of atrial fibrillation using RR intervals

EXPERT SYSTEMS 2012年第2期29卷 183-199页

作者： Yaghouby, Farid Ayatollahi, Ahmad Bahramali, Reihaneh Yaghouby, Maryam Iran Univ Sci & Technol Sch Elect Engn Tehran Iran Univ Appl Sci & Technol Unit Dadeh Pardazi Iran DPI Tehran Iran Azad Univ Mashhad Fac Engn Mashhad Iran Iran Univ Sci & Technol Sch Elect & Elect Engn Tehran Iran Univ Kentucky Ctr Biomed Engn Wenner Gren Res Lab Lexington KY 40506 USA

In this study, two variants of genetic programming, namely linear genetic programming (LGP) and multi-expression programming (MEP) are utilized to detect atrial fibrillation (AF) episodes. LGP- and MEP-based models are derived to classify samples of AF and Normal episodes based on the analysis of RR interval signals. A weighted least-squares (WLS) regression analysis is performed using the same features and data sets to benchmark the models. Another important contribution of this paper is identification of the effective time domain features of heart rate variability (HRV) signals upon an improved forward floating selection (IFFS) analysis. The models are developed using MIT-BIH arrhythmia database. The diagnostic performances of the LGP and MEP classifiers are evaluated through receiver operating characteristics (ROC) analysis. The results indicate that the LGP and MEP models are able to diagnose the AF arrhythmia with an acceptable high accuracy. The proposed models have significantly better diagnosis performances than the regression and several models found in the literature.

关键词： atrial fibrillation heart rate variability signal linear genetic programming multi-expression programming forward floating selection arrhythmia detection

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A reverse engineering algorithm for neural networks, applied to the subthalamopallidal network of basal ganglia

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NEURAL NETWORKS 2008年第2-3期21卷 379-386页

作者： Floares, Alexandru George Inst Oncol Dept Artificial Intelligence Cluj Napoca 400015 Transylvania Romania SAIA Cluj Napoca 400310 Transylvania Romania

Modeling neural networks with ordinary differential equations systems is a sensible approach, but also very difficult. This paper describes a new algorithm based on linear genetic programming which call be used to reverse engineer neural networks. The RODES algorithim automatically discovers the structure of the network, including neural connections, their signs and strengths, estimates its parameters, and can even be used to identify the biophysical mechanisms involved. The algorithm is tested on simulated time series data, generated using a realistic model of the subthalamopallidal network of basal ganglia. The resulting ODE system is highly accurate, and results are obtained in a matter of minutes. This is because the problem of reverse engineering a system of coupled differential equations is reduced to one of reverse engineering individual algebraic equations. The algorithm allows the incorporation of common domain knowledge to restrict the solution space. To our knowledge, this is the first time a realistic reverse engineering algorithm based on linear genetic programming has been applied to neural networks. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： neural networks reverse engineering algorithm linear genetic programming systems of ordinary differential equations basal ganglia discovery science approach

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Predictive modeling of the lateral drift capacity of circular reinforced concrete columns using an evolutionary algorithm

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ENGINEERING WITH COMPUTERS 2021年第2期37卷 1579-1591页

作者： Sharif, Mostafa Rezvani Zavareh, Seyed Mohammad Reza Sadri Tabaei KN Toosi Univ Technol Dept Civil Engn Tehran Iran

The drift capacity of reinforced concrete (RC) columns is a crucial factor in displacement and seismic based design procedure of RC structures, since they might be able to withstand the loads or dissipate the energy applied through deformation and ductility. Considering the high costs of testing methods for observing the drift capacity and ductility of RC structural members in addition to the impact of numerous parameters, numerical analyses and predictive modeling techniques have very much been appreciated by researchers and engineers in this field. This study is concerned with providing an alternative approach, termed as linear genetic programming (LGP), for predictive modeling of the lateral drift capacity (Delta(max)) of circular RC columns. A new model is developed by LGP incorporating various key variables existing in the experimental database employed and those well-known models presented by various researchers. The LGP model is examined from various perspectives. The comparison analysis of the results with those obtained by previously proposed models confirm the precision of the LGP model in estimation of the Delta(max)factor. The results reveal the fact that the LGP model impressively outperforms the existing models in terms of predictability and performance and can be definitely used for further engineering purposes. These approve the applicability of LGP technique for numerical analysis and modeling of complicated engineering problems.

关键词： Reinforced concrete columns Drift capacity Cyclic lateral loading Predictive modeling linear genetic programming

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New empirical formulations for indirect estimation of peak-confined compressive strength and strain of circular RC columns using LGP method

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ENGINEERING WITH COMPUTERS 2018年第4期34卷 865-880页

作者： Rostami, Mohamad Fazel Sadrossadat, Ehsan Ghorbani, Behnam Kazemi, Seyed Morteza Islamic Azad Univ Dept Civil Engn Kashmar Branch Kashmar Iran Islamic Azad Univ Mashhad Branch Young Researchers & Elite Club Mashhad Iran

Reinforced concrete (RC) columns have been basically designed to withstand compressive loads by means of strain and ductility of the longitudinal and transverse reinforcing materials. The objective of this paper is to propose new predictive models of confined compressive strength and strain at confined peak stress of circular-reinforced concrete columns using a powerful evolutionary-based computational technique, namely, linear genetic programming (LGP). For this aim, a collection of data is utilized to develop new models. The models obtained in this study characterize peak-confined compressive strength and corresponding strain factors in terms of the compressive strength of unconfined concrete cylinder specimens, core diameter of circular column, yield strength of transverse reinforcement, ratio of volume of lateral reinforcement to volume of confined concrete core, spacing of lateral reinforcement or spiral pitch, and ratio of longitudinal steel to area of core of section in addition to the column height. These factors have also been considered as the most significant input variables in several models proposed by scholars in the existing literature for approximation of the peak-confined compressive strength and corresponding strain of RC columns. To evaluate the validity of the obtained models, several analyses are conducted and the results are compared with those provided by other researchers to validate and verify the capability of the proposed models. Consequently, the results explicitly approve that the proposed models are of a notably better performance than the traditional models in the literature.

关键词： Confined compressive strength Strain at confined peak stress Reinforced concrete columns Indirect estimation linear genetic programming

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Geometry and sizing optimisation of discrete structure using the genetic programming method

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2006年第6期31卷 452-461页

作者： Zheng, Q. Z. Querin, O. M. Barton, D. C. Univ Leeds Sch Mech Engn Leeds LS2 9JT W Yorkshire England

This paper presents a structural optimisation method using the genetic programming (GP) technique. This method applied linear GP to derive optimum geometry and sizing of discrete structure from an arbitrary initial design space. The linear GP was used to find out the optimum nodal locations and member sizing of the structure through a linear sequence of programming instructions. The nodal locations and member cross-sectional areas of the structure were used as the design variable for these instructions, with the optimal geometry and sizing obtained by evolving a population of GP individuals satisfying the optimisation design objective. The approach was applied to the benchmark example of ten-bar planar truss for verification. Other truss examples, including 18-bar planar truss and 25-bar space truss, were also used to demonstrate the effectiveness of this method. The optimum results obtained demonstrate the practicability and generality of using the proposed method in geometry and sizing optimisation problems.

关键词： linear genetic programming discrete structure geometry and sizing optimisation

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New Insights Into Diversification of Hyper-Heuristics

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IEEE TRANSACTIONS ON CYBERNETICS 2014年第10期44卷 1747-1761页

作者： Ren, Zhilei Jiang, He Xuan, Jifeng Hu, Yan Luo, Zhongxuan Dalian Univ Technol Sch Software Dalian 116621 Peoples R China INRIA Lille Nord Europe F-59650 Lille France

There has been a growing research trend of applying hyper-heuristics for problem solving, due to their ability of balancing the intensification and the diversification with low level heuristics. Traditionally, the diversification mechanism is mostly realized by perturbing the incumbent solutions to escape from local optima. In this paper, we report our attempt toward providing a new diversification mechanism, which is based on the concept of instance perturbation. In contrast to existing approaches, the proposed mechanism achieves the diversification by perturbing the instance under solving, rather than the solutions. To tackle the challenge of incorporating instance perturbation into hyper-heuristics, we also design a new hyper-heuristic framework HIP-HOP (recursive acronym of HIP-HOP is an instance perturbation-based hyper-heuristic optimization procedure), which employs a grammar guided high level strategy to manipulate the low level heuristics. With the expressive power of the grammar, the constraints, such as the feasibility of the output solution could be easily satisfied. Numerical results and statistical tests over both the Ising spin glass problem and the p-median problem instances show that HIP-HOP is able to achieve promising performances. Furthermore, runtime distribution analysis reveals that, although being relatively slow at the beginning, HIP-HOP is able to achieve competitive solutions once given sufficient time.

关键词： Hyper-heuristics instance perturbation Ising spin glass linear genetic programming p-median

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State-of-art of genetic programming applications in water-resources systems analysis

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2020年第2期192卷 1-17页

作者： Mohammad-Azari, Sahar Bozorg-Haddad, Omid Loaiciga, Hugo A. Univ Tehran Fac Agr Engn & Technol Dept Irrigat & Reclamat Engn Coll Agr & Nat Resources Tehran Iran Univ Calif Santa Barbara Dept Geog Santa Barbara CA 93016 USA

Evolutionary algorithms (EAs) have become competitive solvers of a wide variety of water-resources optimization problems. genetic programming (GP) has become a leading EA since its inception in 1985. This paper reviews the state-of-the-art of GP and its applications in water-resources systems analysis. A comprehensive knowledge about GP's theory and modeling approach is essential for its successful application in water-resources systems analysis. This review presents variants of GP that have been proven useful in various applications to water resources problems. Several examples of applications of GP in water-resources systems analysis are herein presented. This review reveals GP's capability and superiority compared to other conventional methods, which makes it suitable for solving a wide variety of water-related problems including rainfall-runoff modeling, streamflow sediment prediction, flood prediction and routing, evaporation and evapotranspiration forecasting, reservoir operation, groundwater modeling, water quality modeling, water demand forecasting, and water distribution systems.

关键词： Evolutionary programming Gene-expression programming Fixed-length gene genetic programming linear genetic programming

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Redundancies in linear GP, canonical transformation, and its exploitation: a demonstration on image feature synthesis

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genetic programming AND EVOLVABLE MACHINES 2011年第1期12卷 49-77页

作者： Watchareeruetai, Ukrit Takeuchi, Yoshinori Matsumoto, Tetsuya Kudo, Hiroaki Ohnishi, Noboru Nagoya Univ Grad Sch Informat Sci Dept Media Sci Chikusa Ku Nagoya Aichi 4648603 Japan

This paper concerns redundancies in representation of linear genetic programming (GP). We identify the causes of redundancies in linear GP and propose a canonical transformation that converts original linear representations into a canonical form in which structural redundancies are removed. In canonical form, we can easily verify whether two representations represent an identical program. We then discuss exploitation of the proposed canonical transformation, and demonstrate a way to improve search performance of linear GP by avoiding redundant individuals. Experiments were conducted with an image feature synthesis problem. Firstly, we have verified that there are really a lot of redundancies in conventional linear GP. We then investigate the effect of avoiding redundant individuals. The results yield that linear GP with avoidance of redundant individuals obviously outperforms conventional linear GP.

关键词： linear genetic programming Redundant representation Canonical form Canonical transformation Feature extraction

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Estimation of Peak Flood Discharges at Ungauged Sites Across Turkey

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WATER RESOURCES MANAGEMENT 2012年第9期26卷 2569-2581页

作者： Seckin, Neslihan Guven, Aytac Gaziantep Univ Civil Engrg Dept TR-27310 Gaziantep Turkey Cukurova Univ Civil Engrg Dept TR-01330 Adana Turkey

The reliable forecasting of the peak flood discharge at river basins is a common problem, and it becomes more complicated when there is inadequate recorded data. The statistical methods commonly used for the estimation of peak flood discharges are generally considered to be inadequate because of the complexity of this problem. Recently, genetic programming (GP) which is a branch of soft computing methods has attracted the attention of the hydrologists. In this study, gene-expression programming (GEP) and linear genetic programming (LGP), which are extensions to GP, in addition to logistic regression (LR) were employed in order to forecast peak flood discharges. The study covered 543 ungauged sites across Turkey. Drainage area, elevation, latitude, longitude, and return period were used as the inputs while the peak flood discharge was the output. Model comparison results revealed that GEP predicted the peak flood discharges with R (2) = 57.4 % correlation, LGP with 56 % and LR model with 42.3 %, respectively. The peak flood discharges in all river basins can now be determined using the single equation provided by the GEP model.

关键词： Peak flood discharge Ungauged site linear genetic programming Gene-expression programming Logistic regression

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Towards the prediction of business failure via computational intelligence techniques

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EXPERT SYSTEMS 2011年第3期28卷 209-226页

作者： Divsalar, Mehdi Firouzabadi, Ali Khatami Sadeghi, Meisam Behrooz, Amir Hossein Alavi, Amir Hossein Allameh Tabatabai Univ Fac Management & Accounting Sch Business Tehran Iran Iran Univ Sci & Technol Sch Civil Engn Tehran Iran

This is a pioneer study that presents two branches of computational intelligence techniques, namely linear genetic programming (LGP) and radial basis function (RBF) neural network to build models for bankruptcy prediction. The main goal is to classify samples of 140 bankrupt and non-bankrupt Iranian corporations by means LGP and RBF. Another important contribution of this paper is to identify the effective predictive financial ratios based on an extensive bankruptcy prediction literature review and a sequential feature selection analysis. In order to benchmark the proposed models, a log-log regression analysis is further performed. A comparative study on the classification accuracy of the LGP, RBF and regression-based models is conducted. The results indicate that the proposed models effectively let estimate any enterprise in the aspect of bankruptcy. The LGP models have a significantly better prediction performance in comparison with the RBF and regression models.

关键词： bankruptcy prediction linear genetic programming radial basis function financial ratios

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