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artificial bee colony programming for symbolic regression

INFORMATION SCIENCES 2012年 209卷 1-15页

作者： Karaboga, Dervis Ozturk, Celal Karaboga, Nurhan Gorkemli, Beyza Erciyes Univ Fac Engn Intelligent Syst Res Grp Kayseri Turkey

artificial bee colony algorithm simulating the intelligent foraging behavior of honey bee swarms is one of the most popular swarm based optimization algorithms. It has been introduced in 2005 and applied in several fields to solve different problems up to date. In this paper, an artificial bee colony algorithm, called as artificial bee colony programming (ABCP), is described for the first time as a new method on symbolic regression which is a very important practical problem. Symbolic regression is a process of obtaining a mathematical model using given finite sampling of values of independent variables and associated values of dependent variables. In this work, a set of symbolic regression benchmark problems are solved using artificial bee colony programming and then its performance is compared with the very well-known method evolving computer programs, genetic programming. The simulation results indicate that the proposed method is very feasible and robust on the considered test problems of symbolic regression. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Genetic programming Symbolic regression artificial bee colony algorithm artificial bee colony programming

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Multi Hive artificial bee colony programming for high dimensional symbolic regression with feature selection

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APPLIED SOFT COMPUTING 2019年 78卷 515-527页

作者： Arslan, Sibel Ozturk, Celal Erciyes Univ Engn Fac Comp Engn Dept Kayseri Turkey

Feature selection is a process that provides model extraction by specifying necessary or related features and improves generalization. The artificial bee colony (ABC) algorithm is one of the most popular optimization algorithms inspired on swarm intelligence developed by simulating the search behavior of honey bees. artificial bee colony programming (ABCP) is a recently proposed high level automatic programming technique for a Symbolic Regression (SR) problem based on the ABC algorithm. In this paper, a new feature selection method based on ABCP is proposed, Multi Hive ABCP (MHABCP) for high-dimensional SR problems. The learning ability and generalization performance of the proposed MHABCP is investigated using synthetic and real high-dimensional SR datasets and is compared with basic ABCP and GP automatic programming methods. Experimental results show that MHABCP has better performance choosing relevant features in high dimensional SR problems and generalization than other methods. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Feature selection Genetic programming artificial bee colony programming Multi hive artificial bee colony programming High dimension data

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Solving differential equations with artificial bee colony programming

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SOFT COMPUTING 2020年第23期24卷 17991-18007页

作者： Boudouaoui, Yassine Habbi, Hacene Ozturk, Celal Karaboga, Dervis MHamed Bougara Univ Boumerdes Appl Automat Lab Boumerdes 35000 Algeria Erciyes Univ Engn Fac Comp Engn Dept TR-38039 Kayseri Turkey King Abdulaziz Univ Fac Comp & Informat Technol Dept Informat Syst Jeddah Saudi Arabia

Relying on artificial bee colony programming (ABCP), we present in this paper, for the first time, a novel methodology for solving differential equations. The three-phase evolving process of ABCP is managed to apply on the issue of recovering the exact solution of differential equations through a well-posed problem. In fact, the original ABCP model which has been initially developed for symbolic regression cannot be used directly as differential problems might have multiple outputs. Moreover, the definition of fitness function is a critical problem-dependent issue for model design. In this sense, a problem-specific ABCP algorithm is worked out in the present contribution. With the proposed algorithm, solution with multiple outputs can evolve under a multiple-tree framework toward the exact solution. For fitness function evaluation, different forms are derived for ordinary and partial differential equations by performing experiments with multiple runs. Results on several differential equations are reported and compared to other advanced methods to assess the feasibility and the potential of the proposed method. A computational performance evaluation is provided for the considered examples and completed with an additional study on the impact of key control parameters.

关键词： Differential equations Analytical solution Automatic programming artificial bee colony programming Swarm optimization

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Solving artificial ant problem using two artificial bee colony programming versions

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APPLIED INTELLIGENCE 2020年第11期50卷 3695-3717页

作者： Boudardara, Fateh Gorkemli, Beyza Erciyes Univ Comp Engn Dept Kayseri Turkey

artificialant problem is considered as a sub-problem of robotic path planning. In this study, it is solved using two different methods: artificial bee colony programming and a new version of it called shrinking artificial bee colony programming. The former is a novel evolutionary computation based automatic programming method based on artificial bee colony algorithm and it was previously applied to this problem by the researchers. However, in this study, more comprehensive analyses and comparison study are provided. The shrinking artificial bee colony programming was developed in this study and its basic idea is to reduce the number of food sources, periodically, instead of a constant number used in the artificial bee colony programming. First, some parameter tuning studies were carried out for the shrinking artificial bee colony programming. Then, performances of the artificial bee colony programming, shrinking artificial bee colony programming and some other evolutionary computation based automatic programming methods were compared on Santa Fe and Los Altos Hills trails. Simulation results and the comparison study show that both of the algorithms can be used to solve the artificial ant problem effectively. Furthermore, the periodically decreasing population size property added to the artificial bee colony programming improves the performance of the algorithm on the artificial ant problem. While the proposed approach shows one of the superior performances among the considered methods, the results of the artificial bee colony programming are competitive to the methods in the comparison study.

关键词： Robotic path planning artificial ant problem Evolutionary computation based automatic programming artificial bee colony programming Shrinking artificial bee colony programming Dynamically decreasing population size

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Cloud-based artificial bee colony programming Web Application

Cloud-based Artificial Bee Colony Programming Web Applicatio...

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Innovations in Intelligent Systems and Applications Conference (ASYU)

作者： Bozogullarindan, Ceylan Ozturk, Celal Erciyes Univ Bilgisayar Muhendisligi Bolumu Kayseri Turkey

ISBN: (纸本)9781728128689

To solve a problem without any knowledge of its solution, a variety evolutionary computation approach based automatic programming methods have been developed. Artficial bee colony programming (ABCP) is one of the recently proposed automatic programming methods. ABCP has been preferred and applied to various problems by researchers from different disciplines like other evolutionary computation approach based algorithms. In this study, a cloud-based web application (ABCP-Web) that allows to use the ABCP with no need to have any programming knowledge is proposed. ABCPWeb is tested on the benchmark problems that are previously solved by ABCP. The success of ABCPWeb is revealed by comparing the experimental results with the results of basic ABCP.

关键词： artificial bee colony programming Machine Learning Cloud-based Web Application

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Scaled artificial bee colony programming

Scaled artificial bee colony programming

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International Conference on Applied Smart Systems (ICASS)

作者： Boudouaoui, Yassine Habbi, Hacene Univ Boumerdes Appl Automat Lab FHC Ave Independance Boumerdes 35000 Algeria

ISBN: (纸本)9781538668665

Problems of symbolic regression aim to develop a function, described in symbolic form, that fits a given target fitcases. artificial bee colony programing algorithm (ABCP) is one of the most feasible automatic programming methods that was proposed to solve symbolic regression problems. ABCP concept is based on swarm bee optimization and proved to achieve satisfactory performance. This paper proposes a novel variant of ABCP algorithm referred to as scaled artificial bee colony programming (SABCP). The SABCP variant results from structural modifications of ABCP which apply to food sources and solution representation as well as prediction model output form. The designed variant is checked for performance on symbolic regression problems and compared to other programming methods.

关键词： artificial bee colony programming scaled artificial bee colony programming swarm optimization symbolic regression automatic programming

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Application of artificial bee colony programming techniques for predicting the compressive strength of recycled aggregate concrete

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APPLIED SOFT COMPUTING 2022年 130卷

作者： Moghaddas, Seyed Amirhossein Nekoei, Masood Golafshani, Emadaldin Mohammadi Behnood, Ali Arashpour, Mehrdad Islamic Azad Univ Dept Civil Engn Sci & Res Branch Tehran Iran Purdue Univ Elmore Family Sch Elect & Comp Engn 610 Purdue Mall W Lafayette IN 47907 USA Monash Univ Dept Civil Engn Melbourne Australia Indiana Dept Transportat Crawfordsville Dist Crawfordsville IN USA

The use of recycled aggregate concrete (RAC) in the construction industry can help to prevent irreparable environmental damages and to mitigate the depletion rate of natural resources. However, the quality of the RAC should be investigated before its practical applications. Compressive strength of the RAC (f ' RAC) is one of the most crucial design parameters, which is measured by time-consuming and cost-extensive experiments. One solution to restrict the number of experiments and achieve reliable f ' RAC estimation is through employing machine learning methods, artificial bee colony Expression programming (ABCEP) is a newly proposed automatic regression technique that is used in this study to predict the f ' RAC. For comparison purposes, four extensions of artificial bee colony programming techniques (i.e., artificial bee colony programming (ABCP), quick artificial bee colony programming (qABCP), Quick semantic artificial bee colony programming (qsABCP), and Semantic artificial bee colony programming (sABCP)) were also served. To analyze the results, the average and best performances of all algorithms, regression analysis, execute run times, Wilcoxon signed-rank test, and the behavior of algorithms dealing with the local optima were investigated. The results show that the ABCEP method is the most effective technique, with the average root mean squared error of 10.36 MPa compared to 16.23 MPa, 10.82 MPa, 17.71 MPa, and 14.20 MPa for the developed ABCP-, qABCP-, qsABCP-, and sABCP-based models, respectively, in colony size of 30. In addition, the run time of this algorithm is remarkably less than the other algorithms.(c) 2022 Elsevier B.V. All rights reserved.

关键词： artificial bee colony programming Automatic regression techniques Machine learning Recycled aggregate concrete Compressive strength

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Prediction of self-compacting concrete elastic modulus using two symbolic regression techniques

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AUTOMATION IN CONSTRUCTION 2016年第Apr.期64卷 7-19页

作者： Golafshani, Emadaldin Mohammadi Ashour, Ashraf Islamic Azad Univ Dept Civil Engn Tehran Sci & Res Branch Tehran Iran Univ Bradford Sch Engn Bradford BD7 1DP W Yorkshire England

This paper introduces a novel symbolic regression approach, namely biogeographical-based programming (BBP), for the prediction of elastic modulus of self-compacting concrete (SCC). The BBP model was constructed directly from a comprehensive dataset of experimental results of SCC available in the literature. For comparison purposes, another new symbolic regression model, namely artificial bee colony programming (ABCP), was also developed. Furthermore, several available formulas for predicting the elastic modulus of SCC were assessed using the collected database. The results show that the proposed BBP model provides slightly closer results to experiments than ABCP model and existing available formulas. A sensitivity analysis of BBP parameters also shows that the prediction by BBP model improves with the increase of habitat size, colony size, and maximum tree depth. In addition, among all considered empirical and design code equations, Leemann and Hoffmann and ACI 318-08's equations exhibit a reasonable performance but Persson and Felekoglu et al.'s equations are highly inaccurate for the prediction of SCC elastic modulus. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Self-compacting concrete Elastic modulus Symbolic regression artificial bee colony programming Biogeographical-based programming

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Automatic regression methods for formulation of elastic modulus of recycled aggregate concrete

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APPLIED SOFT COMPUTING 2018年 64卷 377-400页

作者： Golafshani, Emadaldin Mohammadi Behnood, Ali Islamic Azad Univ Dept Civil Engn Tehran Sci & Res Branch Tehran Iran Purdue Univ Lyles Sch Civil Engn 550 Stadium Mall W Lafayette IN 47907 USA

The use of recycled concrete aggregate to produce new concrete can assist the sustainability in construction industry. However, the mechanical properties of this type of aggregate should be precisely investigated before its using in different applications. The elastic modulus of concrete is one of the most important design parameters in many construction applications. Because of various mix designs, the existing formulas for the elastic modulus of concrete cannot be used for recycled aggregate concrete (RAC). In recent years, there have been a few attempts for predicting the elastic modulus of RAC, especially, with various types of artificial intelligence (AI) methods: In this paper, three automatic regression methods, namely, genetic programming (GP), artificial bee colony programming (ABCP) and biogeography-based programming (BBP) were used for estimating the elastic modulus of RAC. Performances of the different automatic regression models were compared with each other. Moreover, the sensitivity analysis was performed to assess the trend of the elastic modulus as a function of effective input parameters used for developing the different automatic regression models. Overall, the results show that GP, ABCP, and BBP can be used as reliable algorithms for prediction of the elastic modulus of RAC. In addition, the water absorption of the mixed coarse aggregate and the ratio of the fine aggregate to the total aggregate were found as two of the most effective parameters affecting the elastic modulus of RAC. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Recycled aggregate concrete Elastic modulus Automatic regression Genetic programming artificial bee colony programming Biogeography based programming

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Symbolic regression with feature selection of dye biosorption from an aqueous solution using pumpkin seed husk using evolutionary computation-based automatic programming methods

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期231卷

作者： Arslan, Sibel Kutuk, Nursah Sivas Cumhuriyet Univ Dept Software Engn TR-58140 Sivas Turkiye Sivas Cumhuriyet Univ Dept Chem Engn TR-58140 Sivas Turkiye

Industrial waste pollution is a serious and systematic problem that harms the environment and people. The development of cheap, simple, and efficient techniques to solve this problem is important for sustainability. In this study, both experimental and evolutionary computation (EC)-based automatic programming (AP) methods were used to investigate the biosorption process for water treatment. In the experiments, titan yellow (TY), an anionic dye, was biosorbed from an aqueous solution containing pumpkin seed husk (PSH). The structure of PSH was examined using a Fourier transform infrared spectroscopy (FTIR) and a scanning electron microscope (SEM). The result of the experimental studies was that TY biosorption of PSH reached a biosorption efficiency of 95% after 120 min of contact time. The maximum biosorption capacity (qmax) was calculated to be 181.8 mg/g. It was found that the biosorption of TY better followed the Dubinin-Radushkevich isotherm (R2 = 0.98) and pseudo second-order reaction kinetics (R2 = 0.99). Based on the thermodynamic data, the biosorption process was exothermic and spontaneous. After the experiments, the process was modeled using pH, biosorbent concentration, initial dye concentration, contact time, and temperature as inputs and biosorption efficiency (%) as output for the methods. Moreover, the success of these AP methods was compared with a newly proposed evolutionary method. The simulation results indicate that AP methods generate best models (R2train = 0.99 and R2 test time. This study shows that EC-based AP methods can effectively model applications such as the biosorption process. = 0.97). At the same time, the most important parameter of the process in the feature analysis is contact

关键词： Pumpkin seed husk Biosorption Titan yellow System modeling Genetic programming artificial bee colony programming

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