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Ünal HT, Başçiftçi F. Evolutionary design of neural network architectures: a review of three decades of research. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-10049-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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2
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Islam MM, Rahman MJ, Chandra Roy D, Tawabunnahar M, Jahan R, Ahmed NAMF, Maniruzzaman M. Machine learning algorithm for characterizing risks of hypertension, at an early stage in Bangladesh. Diabetes Metab Syndr 2021; 15:877-884. [PMID: 33892404 DOI: 10.1016/j.dsx.2021.03.035] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 03/24/2021] [Accepted: 03/31/2021] [Indexed: 12/30/2022]
Abstract
BACKGROUND AND AIMS Hypertension has become a major public health issue as the prevalence and risk of premature death and disability among adults due to hypertension has increased globally. The main objective is to characterize the risk factors of hypertension among adults in Bangladesh using machine learning (ML) algorithms. MATERIALS AND METHODS The hypertension data was derived from Bangladesh demographic and health survey, 2017-18, which included 6965 people aged 35 and above. Two most promising risk factor identification methods, namely least absolute shrinkage operator (LASSO) and support vector machine recursive feature elimination (SVMRFE) are implemented to detect the critical risk factors of hypertension. Additionally, four well-known ML algorithms as artificial neural network, decision tree, random forest, and gradient boosting (GB) have been used to predict hypertension. Performance scores of these algorithms were evaluated by accuracy, precision, recall, F-measure, and area under the curve (AUC). RESULTS The results clarify that age, BMI, wealth index, working status, and marital status for LASSO and age, BMI, marital status, diabetes and region for SVMRFE appear to be the top-most five significant risk factors for hypertension. Our findings reveal that the combination of SVMRFE-GB gives the maximum accuracy (66.98%), recall (97.92%), F-measure (78.99%), and AUC (0.669) compared to others. CONCLUSION GB-based algorithm confirms the best performer for prediction of hypertension, at an early stage in Bangladesh. Therefore, this study highly suggests that the policymakers make proper judgments for controlling hypertension using SVMRFE-GB-based combination to save time and reduce cost for Bangladeshi adults.
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Affiliation(s)
- Md Merajul Islam
- Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh.
| | - Md Jahanur Rahman
- Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh.
| | - Dulal Chandra Roy
- Department of Statistics, University of Rajshahi, Rajshahi 6205, Bangladesh.
| | - Most Tawabunnahar
- Department of Statistics, Jatiya Kabi Kazi Nazrul Islam University, Mymensingh 2220, Bangladesh.
| | - Rubaiyat Jahan
- Institution of Education and Research, University of Rajshahi, Rajshahi 6205, Bangladesh.
| | - N A M Faisal Ahmed
- Institution of Education and Research, University of Rajshahi, Rajshahi 6205, Bangladesh.
| | - Md Maniruzzaman
- Statistics Discipline, Khulna University, Khulna 9208, Bangladesh.
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Foresti GL. Editorial: From Pioneering Artificial Neural Networks to Deep Learning and Beyond. Int J Neural Syst 2021; 31:2103004. [PMID: 33622197 DOI: 10.1142/s0129065721030040] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- Gian Luca Foresti
- Department of Mathematics, Computer Science and Physics, University of Udine, Udine, Italy
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Concrete Crack Detection Based on Well-Known Feature Extractor Model and the YOLO_v2 Network. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11020813] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
This paper compares the crack detection performance (in terms of precision and computational cost) of the YOLO_v2 using 11 feature extractors, which provides a base for realizing fast and accurate crack detection on concrete structures. Cracks on concrete structures are an important indicator for assessing their durability and safety, and real-time crack detection is an essential task in structural maintenance. The object detection algorithm, especially the YOLO series network, has significant potential in crack detection, while the feature extractor is the most important component of the YOLO_v2. Hence, this paper employs 11 well-known CNN models as the feature extractor of the YOLO_v2 for crack detection. The results confirm that a different feature extractor model of the YOLO_v2 network leads to a different detection result, among which the AP value is 0.89, 0, and 0 for ‘resnet18’, ‘alexnet’, and ‘vgg16’, respectively meanwhile, the ‘googlenet’ (AP = 0.84) and ‘mobilenetv2’ (AP = 0.87) also demonstrate comparable AP values. In terms of computing speed, the ‘alexnet’ takes the least computational time, the ‘squeezenet’ and ‘resnet18’ are ranked second and third respectively; therefore, the ‘resnet18’ is the best feature extractor model in terms of precision and computational cost. Additionally, through the parametric study (influence on detection results of the training epoch, feature extraction layer, and testing image size), the associated parameters indeed have an impact on the detection results. It is demonstrated that: excellent crack detection results can be achieved by the YOLO_v2 detector, in which an appropriate feature extractor model, training epoch, feature extraction layer, and testing image size play an important role.
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Papavasileiou E, Cornelis J, Jansen B. A Systematic Literature Review of the Successors of "NeuroEvolution of Augmenting Topologies". EVOLUTIONARY COMPUTATION 2020; 29:1-73. [PMID: 33151100 DOI: 10.1162/evco_a_00282] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
NeuroEvolution (NE) refers to a family of methods for optimizing Artificial Neural Networks (ANNs) using Evolutionary Computation (EC) algorithms. NeuroEvolution of Augmenting Topologies (NEAT) is considered one of the most influential algorithms in the field. Eighteen years after its invention, a plethora of methods have been proposed that extend NEAT in different aspects. In this article, we present a systematic literature review (SLR) to list and categorize the methods succeeding NEAT. Our review protocol identified 232 papers by merging the findings of two major electronic databases. Applying criteria that determine the paper's relevance and assess its quality, resulted in 61 methods that are presented in this article. Our review article proposes a new categorization scheme of NEAT's successors into three clusters. NEAT-based methods are categorized based on 1) whether they consider issues specific to the search space or the fitness landscape, 2) whether they combine principles from NE and another domain, or 3) the particular properties of the evolved ANNs. The clustering supports researchers 1) understanding the current state of the art that will enable them, 2) exploring new research directions or 3) benchmarking their proposed method to the state of the art, if they are interested in comparing, and 4) positioning themselves in the domain or 5) selecting a method that is most appropriate for their problem.
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Affiliation(s)
- Evgenia Papavasileiou
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Brussels, B-1050, Belgium imec, Leuven, B-3001, Belgium
| | - Jan Cornelis
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Brussels, B-1050, Belgium
| | - Bart Jansen
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, Brussels, B-1050, Belgium imec, Leuven, B-3001, Belgium
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Structural Damage Detection Based on Real-Time Vibration Signal and Convolutional Neural Network. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10144720] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The traditional methods of structural health monitoring (SHM) have obvious disadvantages such as being time-consuming, laborious and non-synchronizing, and so on. This paper presents a novel and efficient approach to detect structural damages from real-time vibration signals via a convolutional neural network (CNN). As vibration signals (acceleration) reflect the structural response to the changes of the structural state, hence, a CNN, as a classifier, can map vibration signals to the structural state and detect structural damages. As it is difficult to obtain enough damage samples in practical engineering, finite element analysis (FEA) provides an alternative solution to this problem. In this paper, training samples for the CNN are obtained using FEA of a steel frame, and the effectiveness of the proposed detection method is evaluated by inputting the experimental data into the CNN. The results indicate that, the detection accuracy of the CNN trained using FEA data reaches 94% for damages introduced in the numerical model and 90% for damages in the real steel frame. It is demonstrated that the CNN has an ideal detection effect for both single damage and multiple damages. The combination of FEA and experimental data provides enough training and testing samples for the CNN, which improves the practicability of the CNN-based detection method in engineering practice.
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An Online Charging Scheme for Wireless Rechargeable Sensor Networks Based on a Radical Basis Function. SENSORS 2019; 20:s20010205. [PMID: 31905899 PMCID: PMC6982754 DOI: 10.3390/s20010205] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 12/20/2019] [Accepted: 12/26/2019] [Indexed: 11/16/2022]
Abstract
The node energy consumption rate is not dynamically estimated in the online charging schemes of most wireless rechargeable sensor networks, and the charging response of the charging-needed node is fairly poor, which results in nodes easily generating energy holes. Aiming at this problem, an energy hole avoidance online charging scheme (EHAOCS) based on a radical basis function (RBF) neural network, named RBF-EHAOCS, is proposed. The scheme uses the RBF neural network to predict the dynamic energy consumption rate during the charging process, estimates the optimal threshold value of the node charging request on this basis, and then determines the next charging node per the selected conditions: the minimum energy hole rate and the shortest charging latency time. The simulation results show that the proposed method has a lower node energy hole rate and smaller charging node charging latency than two other existing online charging schemes.
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Modal Strain Energy-Based Structural Damage Detection Using Convolutional Neural Networks. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9163376] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this paper, a convolutional neural network (CNN) was used to extract the damage features of a steel frame structure. As structural damage could induce changes of the modal parameters of the structure, the convolution operation was used to extract the features of modal parameters, and a classification algorithm was used to judge the damage state of the structure. The finite element method was applied to analyze the free vibration of the steel frame and obtain the first-order modal strain energy for various damage scenarios, which was used as the CNN training sample. Then vibration experiments were carried out, and modal parameters were obtained from the modal analysis of the vibration signals. The experimental data were inputted into the CNN to verify its damage detection capability. The result showed that the CNN was effective in detecting the intact structure, single damage, and multi damages with an accuracy of 100%. For comparison, the same samples were also applied to the traditional back propagation (BP) neural network, which failed to detect the intact structure and multiple-damage cases. It was found that: (1) The proposed CNN could be trained from finite element simulation data and used in real frame structure damage detection, and it performed better in structural damage detection than BP neural networks; (2) the measured data of a real structure could be supplemented by numerical simulation data, and satisfactory results have been demonstrated.
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Leng J, Chen Q, Mao N, Jiang P. Combining granular computing technique with deep learning for service planning under social manufacturing contexts. Knowl Based Syst 2018. [DOI: 10.1016/j.knosys.2017.07.023] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Sikirzhytskaya A, Sikirzhytski V, Lednev IK. Determining Gender by Raman Spectroscopy of a Bloodstain. Anal Chem 2017; 89:1486-1492. [PMID: 28208285 DOI: 10.1021/acs.analchem.6b02986] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The development of novel methods for forensic science is a constantly growing area of modern analytical chemistry. Raman spectroscopy is one of a few analytical techniques capable of nondestructive and nearly instantaneous analysis of a wide variety of forensic evidence, including body fluid stains, at the scene of a crime. In this proof-of-concept study, Raman microspectroscopy was utilized for gender identification based on dry bloodstains. Raman spectra were acquired in mapping mode from multiple spots on a bloodstain to account for intrinsic sample heterogeneity. The obtained Raman spectroscopic data showed highly similar spectroscopic features for female and male blood samples. Nevertheless, support vector machines (SVM) and artificial neuron network (ANN) statistical methods applied to the spectroscopic data allowed for differentiating between male and female bloodstains with high confidence. More specifically, the statistical approach based on a genetic algorithm (GA) coupled with an ANN classification showed approximately 98% gender differentiation accuracy for individual bloodstains. These results demonstrate the great potential of the developed method for forensic applications, although more work is needed for method validation. When this method is fully developed, a portable Raman instrument could be used for the infield identification of traces of body fluids and to obtain phenotypic information about the donor, including gender and race, as well as for the analysis of a variety of other types of forensic evidence.
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Affiliation(s)
- Aliaksandra Sikirzhytskaya
- Department of Chemistry, University at Albany, SUNY , 1400 Washington Avenue, Albany, New York 12222, United States
| | - Vitali Sikirzhytski
- Department of Chemistry, University at Albany, SUNY , 1400 Washington Avenue, Albany, New York 12222, United States
| | - Igor K Lednev
- Department of Chemistry, University at Albany, SUNY , 1400 Washington Avenue, Albany, New York 12222, United States
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11
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Leng J, Jiang P. A deep learning approach for relationship extraction from interaction context in social manufacturing paradigm. Knowl Based Syst 2016. [DOI: 10.1016/j.knosys.2016.03.008] [Citation(s) in RCA: 67] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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12
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Veintimilla-Reyes J, Cisneros F, Vanegas P. Artificial Neural Networks Applied to Flow Prediction: A Use Case for the Tomebamba River. ACTA ACUST UNITED AC 2016. [DOI: 10.1016/j.proeng.2016.11.031] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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14
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Principe JC, Chen B. Universal Approximation with Convex Optimization: Gimmick or Reality? [Discussion Forum]. IEEE COMPUT INTELL M 2015. [DOI: 10.1109/mci.2015.2405352] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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15
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Valencia P, Haak A, Cotillon A, Jurdak R. Genetic programming for smart phone personalisation. Appl Soft Comput 2014. [DOI: 10.1016/j.asoc.2014.08.058] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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16
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Thomas P, Suhner MC. A New Multilayer Perceptron Pruning Algorithm for Classification and Regression Applications. Neural Process Lett 2014. [DOI: 10.1007/s11063-014-9366-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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Mirjalili S, Mirjalili SM, Lewis A. Let a biogeography-based optimizer train your Multi-Layer Perceptron. Inf Sci (N Y) 2014. [DOI: 10.1016/j.ins.2014.01.038] [Citation(s) in RCA: 221] [Impact Index Per Article: 22.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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18
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A New Bat Based Back-Propagation (BAT-BP) Algorithm. ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING 2014. [DOI: 10.1007/978-3-319-01857-7_38] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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Neural Networks. Comput Intell 2013. [DOI: 10.1002/9781118534823.ch4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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21
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Nawi NM, Khan A, Rehman M. A New Levenberg Marquardt based Back Propagation Algorithm Trained with Cuckoo Search. ACTA ACUST UNITED AC 2013. [DOI: 10.1016/j.protcy.2013.12.157] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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22
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A New Cuckoo Search Based Levenberg-Marquardt (CSLM) Algorithm. LECTURE NOTES IN COMPUTER SCIENCE 2013. [DOI: 10.1007/978-3-642-39637-3_35] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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23
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Nawi NM, Khan A, Rehman MZ. A New Back-Propagation Neural Network Optimized with Cuckoo Search Algorithm. LECTURE NOTES IN COMPUTER SCIENCE 2013. [DOI: 10.1007/978-3-642-39637-3_33] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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24
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Hybrid Ant Bee Colony Algorithm for Volcano Temperature Prediction. COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE 2012. [DOI: 10.1007/978-3-642-28962-0_43] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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25
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Papadopoulos VD, Beligiannis GN, Hela DG. Combining experimental design and artificial neural networks for the determination of chlorinated compounds in fish using matrix solid-phase dispersion. Appl Soft Comput 2011. [DOI: 10.1016/j.asoc.2011.05.044] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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MANGAL MANISH, SINGH MANUPRATAP. ANALYSIS OF MULTIDIMENSIONAL XOR CLASSIFICATION PROBLEM WITH EVOLUTIONARY FEEDFORWARD NEURAL NETWORKS. INT J ARTIF INTELL T 2011. [DOI: 10.1142/s0218213007003229] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This paper describes the application of two evolutionary algorithms to the feedforward neural networks used in classification problems. Besides of a simple backpropagation feedforward algorithm, the paper considers the genetic algorithm and random search algorithm. The objective is to analyze the performance of GAs over the simple backpropagation feedforward in terms of accuracy or speed in this problem. The experiments considered a feedforward neural network trained with genetic algorithm/random search algorithm and 39 types of network structures and artificial data sets. In most cases, the evolutionary feedforward neural networks seemed to have better of equal accuracy than the original backpropagation feedforward neural network. We found few differences in the accuracy of the networks solved by applying the EAs, but found ample differences in the execution time. The results suggest that the evolutionary feedforward neural network with random search algorithm might be the best algorithm on the data sets we tested.
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Affiliation(s)
- MANISH MANGAL
- Institute of Computer and Information Sciences, Dr. B. R. Ambedkar University, Khandari, Agra, Uttar Pradesh, India
| | - MANU PRATAP SINGH
- Institute of Computer and Information Sciences, Dr. B. R. Ambedkar University, Khandari, Agra, Uttar Pradesh, India
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Yen GG, Lu H. Hierarchical Rank Density Genetic Algorithm for Radial-Basis Function Neural Network Design. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2011. [DOI: 10.1142/s1469026803000975] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
In this paper, we propose a genetic algorithm based design procedure for a radial-basis function neural network. A Hierarchical Rank Density Genetic Algorithm (HRDGA) is used to evolve the neural network's topology and parameters simultaneously. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies highlighted in literature. In addition, the rank-density based fitness assignment technique is used to optimize the performance and topology of the evolved neural network to deal with the confliction between the training performance and network complexity. Instead of producing a single optimal solution, HRDGA provides a set of near-optimal neural networks to the designers so that they can have more flexibility for the final decision-making based on certain preferences. In terms of searching for a near-complete set of candidate networks with high performances, the networks designed by the proposed algorithm prove to be competitive, or even superior, to three other traditional radial-basis function networks for predicting Mackey–Glass chaotic time series.
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Affiliation(s)
- Gary G. Yen
- Intelligent Systems and Control Laboratory, School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK 74078-503, USA
| | - Haiming Lu
- Intelligent Systems and Control Laboratory, School of Electrical and Computer Engineering, Oklahoma State University, Stillwater, OK 74078-503, USA
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28
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Subudhi B, Jena D. Nonlinear system identification using memetic differential evolution trained neural networks. Neurocomputing 2011. [DOI: 10.1016/j.neucom.2011.02.006] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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29
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Bittencout FR, Zárate LE. Hybrid structure based on previous knowledge and GA to search the ideal neurons quantity for the hidden layer of MLP—Application in the cold rolling process. Appl Soft Comput 2011. [DOI: 10.1016/j.asoc.2010.10.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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30
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Motsinger-Reif AA, Deodhar S, Winham SJ, Hardison NE. Grammatical evolution decision trees for detecting gene-gene interactions. BioData Min 2010; 3:8. [PMID: 21087514 PMCID: PMC3000379 DOI: 10.1186/1756-0381-3-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2010] [Accepted: 11/18/2010] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A fundamental goal of human genetics is the discovery of polymorphisms that predict common, complex diseases. It is hypothesized that complex diseases are due to a myriad of factors including environmental exposures and complex genetic risk models, including gene-gene interactions. Such epistatic models present an important analytical challenge, requiring that methods perform not only statistical modeling, but also variable selection to generate testable genetic model hypotheses. This challenge is amplified by recent advances in genotyping technology, as the number of potential predictor variables is rapidly increasing. METHODS Decision trees are a highly successful, easily interpretable data-mining method that are typically optimized with a hierarchical model building approach, which limits their potential to identify interacting effects. To overcome this limitation, we utilize evolutionary computation, specifically grammatical evolution, to build decision trees to detect and model gene-gene interactions. In the current study, we introduce the Grammatical Evolution Decision Trees (GEDT) method and software and evaluate this approach on simulated data representing gene-gene interaction models of a range of effect sizes. We compare the performance of the method to a traditional decision tree algorithm and a random search approach and demonstrate the improved performance of the method to detect purely epistatic interactions. RESULTS The results of our simulations demonstrate that GEDT has high power to detect even very moderate genetic risk models. GEDT has high power to detect interactions with and without main effects. CONCLUSIONS GEDT, while still in its initial stages of development, is a promising new approach for identifying gene-gene interactions in genetic association studies.
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Özbakır L, Baykasoğlu A, Kulluk S. A soft computing-based approach for integrated training and rule extraction from artificial neural networks: DIFACONN-miner. Appl Soft Comput 2010. [DOI: 10.1016/j.asoc.2009.08.008] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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32
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Nitschke GS, Schut MC, Eiben AE. Collective neuro-evolution for evolving specialized sensor resolutions in a multi-rover task. EVOLUTIONARY INTELLIGENCE 2009. [DOI: 10.1007/s12065-009-0034-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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34
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Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks. MODELING DECISIONS FOR ARTIFICIAL INTELLIGENCE 2007. [DOI: 10.1007/978-3-540-73729-2_30] [Citation(s) in RCA: 260] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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35
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Mangal M, Singh MP. Analysis of pattern classification for the multidimensional parity-bit-checking problem with hybrid evolutionary feed-forward neural network. Neurocomputing 2007. [DOI: 10.1016/j.neucom.2006.02.022] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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36
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Buchtala O, Klimek M, Sick B. Evolutionary Optimization of Radial Basis Function Classifiers for Data Mining Applications. ACTA ACUST UNITED AC 2005; 35:928-47. [PMID: 16240769 DOI: 10.1109/tsmcb.2005.847743] [Citation(s) in RCA: 86] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given (and often large) set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes an evolutionary algorithm (EA) that performs feature and model selection simultaneously for radial basis function (RBF) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the EA significantly: hybrid training of RBF networks, lazy evaluation, consideration of soft constraints by means of penalty terms, and temperature-based adaptive control of the EA. The feasibility and the benefits of the approach are demonstrated by means of four data mining problems: intrusion detection in computer networks, biometric signature verification, customer acquisition with direct marketing methods, and optimization of chemical production processes. It is shown that, compared to earlier EA-based RBF optimization techniques, the runtime is reduced by up to 99% while error rates are lowered by up to 86%, depending on the application. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.
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Affiliation(s)
- Oliver Buchtala
- Faculty for Computer Science and Mathematics, University of Passau, Germany.
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Structure-adaptable neurocontrollers: A hardware-friendly approach. ACTA ACUST UNITED AC 2005. [DOI: 10.1007/bfb0032585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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EPNet for chaotic time-series prediction. ACTA ACUST UNITED AC 2005. [DOI: 10.1007/bfb0028531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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Abstract
This paper presents an evolutionary artificial neural network (EANN) approach based on the pareto-differential evolution (PDE) algorithm augmented with local search for the prediction of breast cancer. The approach is named memetic pareto artificial neural network (MPANN). Artificial neural networks (ANNs) could be used to improve the work of medical practitioners in the diagnosis of breast cancer. Their abilities to approximate nonlinear functions and capture complex relationships in the data are instrumental abilities which could support the medical domain. We compare our results against an evolutionary programming approach and standard backpropagation (BP), and we show experimentally that MPANN has better generalization and much lower computational cost.
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Affiliation(s)
- Hussein A Abbass
- School of Computer Science, University of New South Wales, Australian Defence Force Academy Campus, Northcott Drive, 2600 Canberra, ACT, Australia.
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