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Applicability of soft computing techniques for in vitro micropropagation media simulation and optimization: A comparative study on Salvia macrosiphon Boiss

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INDUSTRIAL CROPS AND PRODUCTS 2023年第1期199卷

作者： Sadat-Hosseini, Mohammad Arab, Mohammad M. Soltani, Mohammad Eftekhari, Maliheh Soleimani, Amanollah Univ Jiroft Fac Agr Dept Hort Jiroft Iran Univ Tehran Coll Aburaihan Dept Hort Tehran Iran Univ Tehran Coll Aburaihan Dept Irrigat & Drainage Engn Tehran Iran Tarbiat Modares Univ Fac Agr Dept Hort Sci Tehran Iran Univ Jiroft Fac Agr Dept Agron & Plant Breeding Jiroft Iran

Plant tissue culture media composition prediction is valuable and can diminish the spending time and costs of releasing protocols. The present study assessed the possibility of using some imperative supervised Machine Learning (ML1) algorithms, including Support Vector Machine (SVM2), gene expression programming (GEP3), and Gradient Boosting Decision Tree (GBDT4), in predicting optimized in vitro media composition for prolifer-ation and rooting of Salvia macrosiphon Boiss. and comparing them with a linear regression method, i.e., Bayesian Ridge Regression (BRR5). Input parameters included different concentrations of macro-and micro-nutrients, vitamins, and Plant Growth Regulators (PGRs6). The accuracy of constructed models' performance was inves-tigated according to Root Mean Square Error (RMSE7), Mean Absolute Percentage Error (MAPE8), and Coefficient of Determination (R2 9). Particle Swarm Optimization (PSO10) system was employed to optimize the developed formulations using superior prediction models. Results revealed that ML methods had higher prediction accuracy than BRR. The GEP models were subsequently selected for optimization by PSO. According to hybrid GEP-PSO models, modified MS medium including 0.54 x NH4NO3, 1.94 x KNO3, 0.93 x CaCl2, KH2PO4, MgSO4, 2.65 x minors, 1.28 x vitamins, and myoinositol and supplemented with 0.93 mg/L 6-benzylaminopurine (BAP11) and 0.05 mg/L indole-3-acetic acid (IBA12) could bring about optimal proliferation. Based on the same models, MS medium containing 0.72 x macros, 1.49 x minors, 1.23 x vitamins and myoinositol, 0.37 x sucrose and 1.73 x FeEDDHA and supplemented with 1.97 mg/L 1-naphthaleneacetic acid (NAA13) and 0.53 mg/L IBA could result in the optimized rooting. This study shows the effectiveness of GBDT as an advanced ML algorithm for predicting the formulation of plant tissue culture media. GEP-constructed models were selected for optimization due to the simplicity and clearness of their results as an entire formula

关键词： Salvia in vitro propagation gene expression programming Support Vector Machine Gradient Boosting Decision Tree Bayesian Ridge Regression

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The propensity of the over-stressed rock masses to different failure mechanisms based on a hybrid probabilistic approach

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2022年 119卷 104214-104214页

作者： Faradonbeh, Roohollah Shirani Taheri, Abbas Karakus, Murat Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA 5005 Australia Curtin Univ Western Australian Sch Mines Minerals Energy & Ch Kalgoorlie WA 6430 Australia

The simultaneous impact of excavation-induced stress concentration and mining disturbances on deep underground mines/tunnels can result in severe and catastrophic failure like strain bursting. In this regard, the proper measurement of proneness to different rock failure mechanisms has great importance in terms of safety and economics. This study proposes a practical hybrid gene expression programming-based logistic regression (GEPLR) model, as a multi-class classifier, to detect the failure mechanism (i.e. squeezing, slabbing and strain burst) in hard rock based on four intact rock properties. Three non-linear binary models are developed to predict the occurrence/non-occurrence of each failure mechanism. The logistic regression technique is linked to the developed GEP models to measure the occurrence probability of each failure mechanism. Finally, the failure mechanism that has the maximum probability of occurrence is selected as the predicted output. The performance analysis of the developed model shows that it is efficiently capable of detecting failure mechanisms with high accuracy. The failure mechanism detection models are presented in MATLAB codes to be easily used in practice by engineers/researchers as an initial guide for failure/stability analysis of underground openings. Finally, the validity of the proposed model is further evaluated by new datasets compiled from different studies.

关键词： Failure mechanism Strain burst Slabbing Squeezing gene expression programming Logistic regression

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Equation discovery of dynamized coefficients in the k-e model for urban airflow and airborne contaminant dispersion

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SUSTAINABLE CITIES AND SOCIETY 2023年 99卷

作者： Zhao, Runmin Liu, Sumei Liu, Junjie Jiang, Nan Chen, Qingyan Tianjin Univ Sch Environm Sci & Engn Tianjin Key Lab Indoor Air Environm Qual Control Tianjin 300072 Peoples R China Tianjin Univ Sch Mech Engn Tianjin 300072 Peoples R China Hong Kong Polytech Univ Dept Bldg Environm & Energy Engn Kowloon Hong Kong Peoples R China

Urban airflow and pollution modeling using steady Reynolds-averaged Navier-Stokes (SRANS) with two equation models has faced accuracy challenges, limiting its reliability for sustainable city design. The low accuracy has been attributed to SRANS ill-conditioning, the linear eddy viscosity hypothesis, and uncertainty contributed by empirical formulations and coefficients. Many studies have attempted to modify the two-equation models specifically for urban problems by correcting the model formulations and calibrating the coefficients. However, these corrections often improve accuracy for targeted regions or quantities while diminishing it elsewhere. This stems from inflexibility in the model form. To mitigate the trade-offs and improve generalizability, this study introduces multiple dynamic correctors to the k-& epsilon;coefficients for expanded design space. In particular, the robustness and redistributability of the corrected model were considered by retaining the theoretical interpretation of turbulence and employing symbolic correctors. The gene expression programming technique was employed for equation discovery. The increased model flexibility mitigated the trade-offs in accuracies for different quantities in the training case. The generalizability of a corrected model was evaluated for airflow and dispersion around a single building, a building array, and a group of complex buildings. The corrected model performed consistently for the three flow types and exhibited generalizability.

关键词： Computational fluid dynamics Turbulence model Urban airflow Pollutant dispersion gene expression programming

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Evaluating the performance of artificial intelligence methods for estimation of monthly mean soil temperature without using meteorological data

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ENVIRONMENTAL EARTH SCIENCES 2017年第8期76卷 325-325页

作者： Mehdizadeh, Saeid Behmanesh, Javad Khalili, Keivan Urmia Univ Water Engn Dept Orumiyeh Iran

Soil temperature has an important role in agricultural, hydrological, meteorological and climatological studies. In the present research, monthly mean soil temperature at four different depths (5, 10, 50 and 100 cm) was estimated using artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) and gene expression programming (GEP). The monthly mean soil temperature data of 31 stations over Iran were employed. In this process, the data of 21 and 10 stations were used for training and testing stages of used models, respectively. Furthermore, the geographical information including latitude, longitude and altitude as well as periodicity component (the number of months) was considered as inputs in the mentioned intelligent models. The results demonstrated that the ANN and ANFIS models had good performance in comparison with the GEP model. Nevertheless, the ANFIS generally performed better than ANN model.

关键词： Adaptive neuro-fuzzy inference system Artificial neural networks gene expression programming Soil temperature

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Model genetic Rules Based Systems for Evaluation of Projects

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Procedia Computer Science 2020年 170卷 390-395页

作者： Jesus Silva John Freddy Escobar Gomez Ernesto Steffens Sanabria Hugo hernandez Palma Midori Ikeda Jorge Linares Nohora Mercado Corporación Universitaria Minuto de Dios –Uniminuto Barranquilla Colombia Universidad de la Costa Barranquilla Colombia

The process of project evaluation is of vital importance for decision-making in organizations. In the particular case of IT projects, the historical average of successful projects is 30.7%, while renegotiated projects are 47.3% and cancelled projects are 22% [1]. These figures mean that huge budgets are affected every year by errors in planning or control and monitoring of projects, with an economic and social impact. The objective of this research is to evaluate the MCGEP evolutionary algorithm in different versions databases with information on the evaluation of IT projects. The aim is to determine the possibility of applying an evolutionary algorithm that uses programming of genetic expressions as opposed to others of greater use.

关键词： genetic Algorithms gene expression programming MCGEP Algorithm Project Evaluation Rules learning

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Electric Load Forecasting for Western Canada: A Comparison of Two Non-Linear Methods

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Atmosphere 2012年第3期50卷 352-363页

作者： A. Bakhshaii[a][*] & R. Stull[a] [a] University of British Columbia Vancouver British Columbia Canada

Seven years of hourly temperature and electric load data for British Columbia in western Canada were used to compare two statistical methods, artificial neural networks (ANN) and gene expression programming (GEP), to produce hour-ahead load forecasts. Two linear control methods (persistence and multiple linear regression) were used for verification purposes. A two-stage (predictor-corrector) approach was used. The first stage used a single regression model that applied weather and calendar data for the previous hour to predict load for any hour of the day, and the second stage applied different corrections every hour, based on a high correlation of today's load with yesterday's load for any given hour. By excluding the day-before variables, the two-stage method reduced the total number of variables in the first-stage regression and gave better results. The first five years of data were used for training (regression finding) and validation (comparative testing of candidate functions to reduce overfitting) and the last two for verification (scoring with independent data).It was found that both non-linear methods worked better than the linear methods for the first stage. All methods worked well for the second stage; hence, persistence is recommended for the second stage because it is easiest to do. After both stages, the load error was less than 0.6% of the load magnitude for hour-ahead forecasts. When used iteratively to create forecasts up to 24 hours ahead, errors grew to about 3.5% of the load magnitude for both GEP and ANNs. We also experimented by training the statistical methods with a shorter period (one year) of past data to examine the over-fitting problem. Overall, ANNs were more useful in fitting the curves to robust data, and GEP was superior for short datasets and was less sensitive to the length of the dataset. We also found that a time-nested electric load forecasting model with different lead times kept maximum load errors to 2.1% of the load magnitude

关键词： genetic programming electric load forecasting artificial neural networks gene expression programming

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GEP-based classifier with drift detection for mining imbalanced data streams

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Procedia Computer Science 2020年 176卷 41-49页

作者： Joanna Jedrzejowicz Piotr Jedrzejowicz Institute of Informatics Faculty of Mathematics Physics and Informatics University of Gdansk 80-308 Gdansk Poland Department of Information Systems Gdynia Maritime University Morska 83 81-225 Gdynia Poland

Mining data streams require to cope with time, data size and possible concept drift constraints. Even more challenging is the case where, apart from the above, one has to deal with imbalanced data. Mining non stationary and imbalanced data streams is a relatively new area of research. In this paper, we propose the gene expression programming (GEP) classifier with drift detection and data reuse for mining imbalanced data streams. GEP is used to evolve a complex expression tree returning predictions. Drift detector role is to signal the occurrence of drift which triggers inducing a new learner. Data reuse mechanism allows for improving the balance between minority and majority instances in a subset of data used for evolving the learner. The proposed approach is validated experimentally. The experiment results confirm that our classifier produces high-quality predictions.

关键词： Imbalanced non stationary data streams gene expression programming data reuse

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Biometric Identification from Human Aesthetic Preferences

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SENSORS 2020年第4期20卷 1133-1133页

作者： Sieu, Brandon Gavrilova, Marina Univ Calgary Dept Comp Sci 2500 Univ Dr NW Calgary AB T2N 1N4 Canada

In recent years, human-machine interactions encompass many avenues of life, ranging from personal communications to professional activities. This trend has allowed for person identification based on behavior rather than physical traits to emerge as a growing research domain, which spans areas such as online education, e-commerce, e-communication, and biometric security. The expression of opinions is an example of online behavior that is commonly shared through the liking of online images. Visual aesthetic is a behavioral biometric that involves using a person's sense of fondness for images. The identification of individuals using their visual aesthetic values as discriminatory features is an emerging domain of research. This paper introduces a novel method for aesthetic feature dimensionality reduction using gene expression programming. The proposed system is capable of using a tree-based genetic approach for feature recombination. Reducing feature dimensionality improves classifier accuracy, reduces computation runtime, and minimizes required storage. The results obtained on a dataset of 200 Flickr users evaluating 40,000 images demonstrate a 95% accuracy of identity recognition based solely on users' aesthetic preferences.

关键词： pattern recognition behavioral biometrics biometric security gene expression programming visual aesthetics human-machine interactions

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Trading Decision of Taiwan Stocks with the Help of United States Stock Market

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Procedia Computer Science 2018年 126卷 87-96页

作者： Shih-Chan Huang Chang-Biau Yang Hung-Hsin Chen Department of Computer Science and Engineering National Sun Yat-sen University 70 Lienhai Rd. Kaohsiung 80424 Taiwan

The paper studies how to improve the trading decision of Taiwan stocks with the information of US stock market. Our method first aligns the trading days between Taiwan and US stock markets. Next, the similarity between the portfolio index (PI, constructed from 100 Taiwan stocks) and one of the US stock indices, the Dow Jones Industrial Average (DJIA), NASDAQ composite index (NASDAQ), or Standard & Poor’s 500 (S&P 500), is computed, respectively. The trading signals of PI or each US stock index are generated by the method of Lee et al. Finally, the consensus signals of PI are determined by the majority vote scheme with the weighted functions, calculated from the similarity. The testing period of PI starts from 2000/1/4 to 2017/12/29, totally 4480 days. As the experimental results show, the index combination (PI, DJIA, NASDAQ) with the weighted function W (4) is considered to be the best combination for trading PI. Its average annualized return (cumulative return) achieves 15.03% (1170.42%), which is better than the method of Lee et al. 13.88% (947.65%), and the buy-and-hold strategy 9.85% (442.90%).

关键词： stock investment Taiwan stock US stock indices trading signal gene expression programming

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Rapid Prototype Development Approach for genetic programming

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Journal of Computer and Communications 2024年第2期12卷 67-79页

作者： Pei He Lei Zhang School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China School of Cyberspace Security Guangzhou University Guangzhou China

genetic programming (GP) is an important approach to deal with complex problem analysis and modeling, and has been applied in a wide range of areas. The development of GP involves various aspects, including design of genetic operators, evolutionary controls and implementations of heuristic strategy, evaluations and other mechanisms. When designing genetic operators, it is necessary to consider the possible limitations of encoding methods of individuals. And when selecting evolutionary control strategies, it is also necessary to balance search efficiency and diversity based on representation characteristics as well as the problem itself. More importantly, all of these matters, among others, have to be implemented through tedious coding work. Therefore, GP development is both complex and time-consuming. To overcome some of these difficulties that hinder the enhancement of GP development efficiency, we explore the feasibility of mutual assistance among GP variants, and then propose a rapid GP prototyping development method based on πGrammatical Evolution (πGE). It is demonstrated through regression analysis experiments that not only is this method beneficial for the GP developers to get rid of some tedious implementations, but also enables them to concentrate on the essence of the referred problem, such as individual representation, decoding means and evaluation. Additionally, it provides new insights into the roles of individual delineations in phenotypes and semantic research of individuals.

关键词： genetic programming Grammatical Evolution gene expression programming Regression Analysis Mathematical Modeling Rapid Prototype Development

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