Automatic Programming Method Research Articles

Investigation and Optimization of Biosorbent Capacities of Some Plants Used in Daily Life

In this study, sage, chamomile, and tarragon leaves, which are used as spices and consumed as beverages in daily life, were considered as different biosorbents that can be used in water purification by biosorption. At the same time, the effects of the parameters of initial dye concentration (10-200 mg/L), temperature (20-50 ⁰C) and contact time (0-120 min) on biosorption capacity were investigated. The biosorption processes were found to follow Freundlich isotherm and pseudo-second order (PSO) reaction kinetics. In the study, the process was also modeled using multi-tree evolutionary computation based automatic programming (AP) methods. The methods used initial dye concentration, temperature, and contact time as variables. According to the simulation results, these methods obtained nonlinear mathematical models of the processes with R^2 values as high as 0.99 for each biosorbent. By providing the most accurate models to accurately predict biosorption capacity, this study will make a significant contribution to the field of water treatment using experimental and AP methods.

Immune Plasma Programming: A new evolutionary computation-based automatic programming method

Immune plasma therapy, one of the treatment modalities, has proven effective in combating the now rapidly spreading COVID-19 and many other pandemics. The immune plasma algorithm (IPA), inspired by the application phases of this treatment modality, is a recently proposed metaheuristic algorithm. Since its introduction, it has achieved promising results in engineering applications. In this paper, we propose for the first time immune plasma programming (IPP) based on the structure of IPA as a new evolutionary computation-based automatic programming (AP) method. It is compared with well-known AP methods such as artificial bee colony programming, genetic programming, and cartesian ant programming using symbolic regression test problems. It is also compared with baseline methods, many of which are based on recurrent neural networks and a real-word problem is solved. The control parameters of IPP are also tuned separately. The results of the experiments and statistical tests have shown that the prediction accuracy and convergence speed of the models produced by IPP are high. Therefore, IPP has been proposed as a method that can be used to solve various problems.

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

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

ИСПОЛЬЗОВАНИЕ ТЕХНОЛОГИИ АВТОМАТНОГО ПРОГРАММИРОВАНИЯ ПРИ РАЗРАБОТКЕ ПРИЛОЖЕНИЯ ДЛЯ ПРОГРАММИРОВАНИЯ С ПОМОЩЬЮ ИНСТРУМЕНТАЛЬНЫХ СРЕДСТВ

The article shows that programming can be mastered even by those who have not been previously familiar with it. For this, an application is developed allowing beginners to program using hints. The source code of the application is available in the public domain, developed with the use of automatic programming technology applying such tools as a computer model “dictionary of concepts” and explicit state selection. The programme will be written in the free Sharp Develop integrated development environment based on the C# language. The main problems identified during the programme development, as well as methods for solving them will be given in the article, the main logical deci-sions made to write the programme code will be explained. In addition to the main aim for which this programme code is written, the article can be considered as a manual for writing logic by the programme based on the automatic pro-gramming methods and tools. This article may be useful to people who want to improve their knowledge in the field of programming, and in addition, it is aimed at popularizing the technology of automatic programming to enlarge this area of programme logic development and simplify its principles and methods for the general public.

Применение технологии автоматного программирования для разработки программы управления складскими помещениями на предприятиях

The article clearly shows that developing a control system for automatic storage platforms can be implemented on the basis of the principles and models of automatic programming, subject to applying the system principles and identifying stable states in which this system can be. The aim of the article, in addition to the visual application of automatic programming principles and methods for writing a control system and demonstrating the Switch technology capabilities, is its promotion and dissemination, as well as the possibilities of its application in a wide range of programming various smart devices, industrial installations and programmes operating in various industries. Auto-matic programming implies a detailed study of the technological process to explicitly identify the stable states of the system under study, which are subsequently used to build automated installation logic; this is the subject of the first part of the article. The next step is to write the logic itself, based on the conditions for switching the system from one stable state to the next. The selected automated system has a certain complexity due to the possibility of involving a large number of objects in its construction, however, changing the number of objects in the system to expand or mini-mize production, does not require a complete replacement of the control programme due to the flexibility of its logic written by automatic programming

Investigating the best automatic programming method in predicting the aerodynamic characteristics of wind turbine blade

Automatic programming (AP) is a subfield of artificial intelligence (AI) that can automatically generate computer programs and solve complex engineering problems. This paper presents the accuracy of four different AP methods in predicting the aerodynamic coefficients and power efficiency of the AH 93-W-145 wind turbine blade at different Reynolds numbers and angles of attack. For the first time in the literature, Genetic Programming (GP) and Artificial Bee Colony Programming (ABCP) methods are used for such predictions. In addition, Airfoil Tools and JavaFoil are utilized for airfoil selection and dataset generation. The Reynolds number and angle of attack of the wind turbine airfoil are input parameters, while the coefficients CL, CD and power efficiency are output parameters. The results show that while all four methods tested in the study accurately predict the aerodynamic coefficients, Multi Gene GP (MGGP) method achieves the highest accuracy for RTrain2 and RTest2 (R2 values in CD Train: 0.997-Test: 0.994, in CL Train: 0.991-Test: 0.990, in PE Train: 0.990-Test: 0.970). By providing the most precise model for properly predicting the aerodynamic performance of higher cambered wind turbine airfoils, this innovative and comprehensive study will close a research gap. This will make a significant contribution to the field of AI and aerodynamics research without experimental cost, labor, and additional time.

A robot welding path planning and automatic programming method for open impeller

A robot welding path planning and automatic programming method for open impeller

A Digital Twin-Based Automatic Programming Method for Adaptive Control of Manufacturing Cells

The booming personalized and customized demands of customers in Industry 4.0 pose great challenges for manufacturing enterprises in terms of flexibility and responsiveness. Nowadays, many effective dynamic scheduling approaches have been proposed for manufacturing systems to quickly respond to changes in customer demands, where, however, the implementation of an automatic programming method with high control accuracy and low control delay is still challenging. The above unaddressed issue brings about a lot of labor-intensive and time-consuming manual offline programming work when adjusting the scheduling scheme to meet dynamic customer demands, resulting in limited flexibility and responsiveness in current manufacturing systems. To bridge this gap, a bi-level adaptive control architecture enabled by an automatic programming method is proposed and embedded into a digital twin manufacturing cell (DTMC). The bi-level architecture aims to automatically map an input task scheduling scheme with a batch of jobs into a group of control programs through a behavior model network and a set of event models embedded in DTMC. It also provides an adaptive program modification mechanism to quickly adapt to the dynamic adjustment of the scheduling scheme caused by the changing of customer demands or production conditions, thus equipping DTMC with strong flexibility and responsiveness. Based on the bi-level architecture, a DTMC prototype system is developed, where its application and evaluation results demonstrate the feasibility and effectiveness of the proposed approach.

Research on Computer Programming Optimization System Based on Big Data Technology

To improve network programming efficiency, the paper proposes a network automatic programming method based on big data analysis technology. Build a big data analysis model for automatic network programming. The dissertation uses the distributed information fusion method to extract the significant data feature of automatic network programming, extract the associated feature quantity of automatic network programming big data, and combine the fuzzy membership analysis method to carry out the synthesis operation and fuzzy comprehensive evaluation decision of automatic network programming and build network automation Programming multi-level index evaluation system to realize automatic network programming optimization.

Introduction of ABCEP as an automatic programming method

Automatic programming is a branch of artificial intelligence that presents each solution as a mathematical formula based on heuristic mechanisms. In this study, artificial bee colony expression programming (ABCEP) is presented, which is combined simultaneously with expression programming. By using expression sharing to generate new solutions, the proposed method can minimize certain deficiencies of artificial bee colony programming, such as weak convergence and high locality. A total number of 15 real-world regression benchmark functions was used to evaluate the performance of the proposed model. For comparison purposes, successful run percentage, mean best cost, convergence performance, and run time of ABCEP were compared to those of other tested automatic programming algorithms, including artificial bee colony programming, gene expression programming, genetic programming, and quick artificial bee colony programming. A Wilcoxon signed-rank test was also done to compare the behavior of the algorithms. Additionally, the accuracy of all algorithms was then evaluated using three real-world practical benchmarks. The results indicate that the predictions generated by ABCEP are better than those obtained by other control algorithms based on successful runs, mean fitness values, and convergence rate.

Solving artificial ant problem using two artificial bee colony programming versions

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.

Feature selection and classification of metabolomics data using artificial bee colony programming (ABCP)

One area of metabolic data analysis is processes that involve the detection and discovery of biomarkers used in the early diagnosis of diseases and development of alternative treatments. Classification and feature selection are frequently used in the statistical analysis of metabolomics data for the detection and discovery of biomarkers. Recently, automatic programming methods have begun to be used instead of conventional methods. In this paper, three conventional classification and feature selection methods (PLS-DA, RF, SVM) and two automatic programming methods (ABCP and GP) are applied to classification problems where they are evaluated on synthetic and real data sets. The selection performances on the biomarker discovery of the algorithms have been compared. It has been found that automatic programming methods are more successful in classifying metabolic data and ABCP is superior to GP in biomarker discovery.

A quick semantic artificial bee colony programming (qsABCP) for symbolic regression

Artificial bee colony programming (ABCP) is a novel evolutionary computation based automatic programming method, which uses the basic structure of artificial bee colony (ABC) algorithm. In this paper, some studies were conducted to improve the performance of ABCP and three new versions of ABCP are introduced. One of these improvements is related to the convergence performance of ABCP. In order to increase the local search ability and achieve higher quality solutions in early cycles, quick ABCP algorithm was developed. Experimental studies validate the enhancement of the convergence performance when the quick ABC approach is used in ABCP. The second improvement introduced in this paper is about providing high locality. Using semantic similarity based operators in the information sharing mechanism of ABCP, semantic ABCP was developed and experiment results show that semantic based information sharing improves solution quality. Finally, combining these two methods, quick semantic ABCP is introduced. Performance of these novel methods was compared with some well known automatic programming algorithms on literature test problems. Additionally, ABCP based methods were used to find approximations of the Colebrook equation for flow friction. Simulation results show that, the proposed methods can be used to solve symbolic regression problems effectively.

Artificial Bee Colony Programming Descriptor for Multi-Class Texture Classification

Texture classification is one of the machine learning methods that attempts to classify textures by evaluating samples. Extracting related features from the samples is necessary to successfully classify textures. It is a very difficult task to extract successful models in the texture classification problem. The Artificial Bee Colony (ABC) algorithm is one of the most popular evolutionary algorithms inspired by the search behavior of honey bees. Artificial Bee Colony Programming (ABCP) is a recently introduced high-level automatic programming method for a Symbolic Regression (SR) problem based on the ABC algorithm. ABCP has applied in several fields to solve different problems up to date. In this paper, the Artificial Bee Colony Programming Descriptor (ABCP-Descriptor) is proposed to classify multi-class textures. The models of the descriptor are obtained with windows sliding on the textures. Each sample in the texture dataset is defined instance. For the classification of each texture, only two random selected instances are used in the training phase. The performance of the descriptor is compared standard Local Binary Pattern (LBP) and Genetic Programming-Descriptor (GP-descriptor) in two commonly used texture datasets. When the results are evaluated, the proposed method is found to be a useful method in image processing and has good performance compared to LBP and GP-descriptor.

A Comparative Study of Automatic Programming Techniques

Automatic programming, an evolutionary computing technique, forms the programs automatically and is based on higher level features that can be easily specified than normal programming languages. Genetic Programming (GP) is the first and best-known automatic programming technique that is applied to solve many practical problems. Artificial Bee Colony Programming (ABCP) is one of the latest proposed automatic programming method that combines evolutionary approach with swarm intelligence. GP is an extension version of Genetic Algorithm (GA) and ABCP is based on Artificial Bee Colony (ABC) algorithm. The main differences of these automatic programing techniques and their conventional algorithms (GA and ABC) are modeling solution. In ABC same as GA, the solutions are represented fixed code blocks. In GP and ABCP, the positions of food sources are expressed in tree structure that is composed of different combinations of terminals and functions that are specifically defined as problems. This paper presents a review on GP and ABCP and they are worked in symbolic regression, prediction and feature selection problems which are widely tackled by researchers. The results of the ABCP compared with results of GP show that this algorithm is a powerful optimization technique for structural design.

Multi Hive Artificial Bee Colony Programming for high dimensional symbolic regression with feature selection

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.

Knowledge-Based Programming for the Cybersecurity Solution

Introduction:The problem of cyberattacks reduces to the unwanted infiltration of software through latent vulnerable access points. There are several approaches to protection here. First, unknown or improper system states can be detected through their characterization (using neural nets and/or symbolic codes), then interrupting the execution to run benchmarks and observe if they produce the states they should. If not, the execution can be rewound to the last successful benchmark, all states restored, and rerun.Methods:This will only work for cyber-physical systems that can be rewound. Benchmarks will often include sensory information. The second approach is termed, “semantic randomization”. This is similar to the well-known compiler technique known as “syntactic randomization”. The significant difference is that different variants of the algorithm itself are being automatically programmed. Cyberattacks will generally not be successful at more than one variant. This means that cybersecurity is moving us towards automatic programming as a desirable consequence. Knowledge-Based Software Engineering (KBSE) is the way to achieve automatic programming in practice.Discussion:There is non-determinism in the execution of such systems, which provides cybersecurity. Knowledge-based algorithmic compilers are the ultimate solution for scalable cost-effective cybersecurity. However, unlike the case for the less-secure syntactic randomization, the cost-effectiveness of semantic randomization is a function of scale. A simple randomization-based automatic programming method is illustrated and discussed.Conclusion:Semantic randomization is overviewed and compared against other technologies used to protect against cyberattack. Not only does semantic randomization itself, or in combination with other methodologies, offer improved protection; but, it serves as the basis for a methodology for automatic programming, which in turn makes the semantic randomization methodology for cybersecurity cost-effective.

Optimized Dispatch Schedule for Autonomous Grids in Isolated Islands

The rapid development of wind power provides a new solution for power supply of isolated island. However, due to the intermittent and stochastic nature of renewable energy resources (RES), the energy storage unit (ESU) is required for power grid reliability. This paper proposed an automatic programming method for autonomous grid in isolated islands. The sea water pumped storage plant serves as ESU to counter-balance the fluctuations of RESs. The penetration level of RES and the profit of the Island system operator (ISO) increase significantly. With the geographical and historical data of an island in China, the effectiveness of the proposed method is testified.

Research of CNC Automatic Programming Method Based on Process Database

In this paper, a method of NC process information extraction and import to template file based on Process Recording Files and database was proposed, which provides an effective way for NC process information sharing and reuse, and made positive effect on current situation in CNC manufacturers, such as: type of product is relative stable, process of CAM programming has lots of duplication, process parameters are dependent on programmers, the processing quality is instable and so on. The Process Recording Files are from mainstream heterogeneous CAM softwares, such as: PowerMILL, UG/CAM, which used to record CAM programming process, macro files from PowerMILL and UG/CAM are belongs to this kind of file.

Drawing and CNC Software System for Light Gage Steel Structure

The primary program development principles and techniques of detailed drawings and CNC programming based on three-dimensional solid model for light gage steel structure are discussed. Components of the structure with complex spatial relationships and construction details are shown directly, accurately and completely based on 3D solid model, and structure properties and design requirements are integrated into the intelligent model. Therefore, drawings and detailed drawings can be drawn and labeled automatically by projecting and hiding with the extracted geometric information of particular entities. Further more, an automatic programming method for computerized numerical control is proposed, and CNC program are encoded according to manufacture information of parts in the model.