Automatic Program Generation Research Articles

Dynamical Sphere Regrouping Particle Swarm Optimization Programming: An Automatic Programming Algorithm Avoiding Premature Convergence

Symbolic regression plays a crucial role in machine learning and data science by allowing the extraction of meaningful mathematical models directly from data without imposing a specific structure. This level of adaptability is especially beneficial in scientific and engineering fields, where comprehending and articulating the underlying data relationships is just as important as making accurate predictions. Genetic Programming (GP) has been extensively utilized for symbolic regression and has demonstrated remarkable success in diverse domains. However, GP’s heavy reliance on evolutionary mechanisms makes it computationally intensive and challenging to handle. On the other hand, Particle Swarm Optimization (PSO) has demonstrated remarkable performance in numerical optimization with parallelism, simplicity, and rapid convergence. These attributes position PSO as a compelling option for Automatic Programming (AP), which focuses on the automatic generation of programs or mathematical models. Particle Swarm Programming (PSP) has emerged as an alternative to Genetic Programming (GP), with a specific emphasis on harnessing the efficiency of PSO for symbolic regression. However, PSP remains unsolved due to the high-dimensional search spaces and local optimal regions in AP, where traditional PSO can encounter issues such as premature convergence and stagnation. To tackle these challenges, we introduce Dynamical Sphere Regrouping PSO Programming (DSRegPSOP), an innovative PSP implementation that integrates DSRegPSO’s dynamical sphere regrouping and momentum conservation mechanisms. DSRegPSOP is specifically developed to deal with large-scale, high-dimensional search spaces featuring numerous local optima, thus proving effective behavior for symbolic regression tasks. We assess DSRegPSOP by generating 10 mathematical expressions for mapping points from functions with varying complexity, including noise in position and cost evaluation. Moreover, we also evaluate its performance using real-world datasets. Our results show that DSRegPSOP effectively addresses the shortcomings of PSO in PSP by producing mathematical models entirely generated by AP that achieve accuracy similar to other machine learning algorithms optimized for regression tasks involving numerical structures. Additionally, DSRegPSOP combines the benefits of symbolic regression with the efficiency of PSO.

ARC WELDING ROBOTIC FLEXIBLE CELL SIMULATION USING ROBOGUIDE SOFTWARE

The objective of the work is the design and simulation of a flexible manufacturing structure with two robots, integrated in a welding application, using the Roboguide software. FANUC ROBOGUIDE is a robot simulator that simulates both the movement of the robots and the commands from the designed application, significantly reducing the time needed to create new motion configurations.The possibility to import the CAD models of the components, of the fixing devices and of the final effectors of the robots integrated into the designed application, allows the creation and evaluation of various structures. Automatic program generation simplifies manual programming operations. ROBOGUIDE allows pre-programming of robots before they are installed in a cell, as well as visualization and confirmation of robot trajectories before downloading the programs to the real robot.

Gene expression programming with dual strategies and neighborhood search for symbolic regression problems

As an evolutionary algorithm for automatic generation of computer programs, Gene expression programming (GEP) has received a lot of attention because it can generate concise and highly accurate solutions. However, GEP also has some unsatisfactory aspects, such as too many control parameters and a lack of balance between exploration and exploitation. Therefore, a new GEP with dual strategies and neighborhood search (DNSGEP) is proposed in this paper for symbolic regression problems. As the core part, a new multi-parent crossover operator with neighborhood search is proposed to replace the original transposition and recombination operators in GEP, which greatly reduces the excessive control parameters in GEP. To assist the multi-parent crossover operator in selecting parents, selection weight is used to assess the quality of individuals in the population. The multi-parent crossover operator combines selection weight to learn more from the better parents. In order to satisfy the different needs of global search and local search of the algorithm, two different crossover strategies are extended on the basis of the multi-parent crossover operator. Furthermore, a population distribution entropy strategy to GEP is proposed for determining when the algorithm should choose which crossover strategy to use. DNSGEP is compared with GEP and three GEP variants on 14 symbolic regression problems and 6 even parity problems. Experimental results show that DNSGEP has the success rate and accuracy that are fully superior to GEP, GEPADF and OGEP, not inferior to SLGEP.

Code4ML: a large-scale dataset of annotated Machine Learning code.

The use of program code as a data source is increasingly expanding among data scientists. The purpose of the usage varies from the semantic classification of code to the automatic generation of programs. However, the machine learning model application is somewhat limited without annotating the code snippets. To address the lack of annotated datasets, we present the Code4ML corpus. It contains code snippets, task summaries, competitions, and dataset descriptions publicly available from Kaggle-the leading platform for hosting data science competitions. The corpus consists of ~2.5 million snippets of ML code collected from ~100 thousand Jupyter notebooks. A representative fraction of the snippets is annotated by human assessors through a user-friendly interface specially designed for that purpose. Code4ML dataset can help address a number of software engineering or data science challenges through a data-driven approach. For example, it can be helpful for semantic code classification, code auto-completion, and code generation for an ML task specified in natural language.

A Comprehensive Survey on Program Synthesis With Evolutionary Algorithms

The automatic generation of computer programs is one of the main applications with practical relevance in the field of evolutionary computation. With program synthesis techniques not only software developers could be supported in their everyday work but even users without any programming knowledge could be empowered to automate repetitive tasks and implement their own new functionality. In recent years, many novel program synthesis approaches based on evolutionary algorithms have been proposed and evaluated on common benchmark problems. Therefore, we identify and discuss in this survey the relevant evolutionary program synthesis approaches in the literature and provide an in-depth analysis of their performance. The most influential approaches we identify are stack-based, grammar-guided, as well as linear genetic programming (GP). For the stack-based approaches, we identify 37 in-scope papers, and for the grammar-guided and linear GP approaches, we identify 12 and 5 papers, respectively. Furthermore, we find that these approaches perform well on benchmark problems if there is a simple mapping from the given input to the correct output. On problems where this mapping is complex, e.g., if the problem consists of several subproblems or requires iteration/recursion for a correct solution, results tend to be worse. Consequently, for future work, we encourage researchers not only to use a program’s output for assessing the quality of a solution but also the way toward a solution (e.g., correctly solved subproblems).

On Comprehension of Genetic Programming Solutions: A Controlled Experiment on Semantic Inference

Applied to the problem of automatic program generation, Genetic Programming often produces code bloat, or unexpected solutions that are, according to common belief, difficult to comprehend. To study the comprehensibility of the code produced by Genetic Programming, attribute grammars obtained by Genetic Programming-based semantic inference were compared to manually written ones. According to the established procedure, the research was carried out as a controlled classroom experiment that involved two groups of students from two universities, and consisted of a background questionnaire, two tests and a feedback questionnaire after each test. The tasks included in the tests required the identification of various properties of attributes and grammars, the identification of the correct attribute grammar from a list of choices, or correcting a semantic rule in an attribute grammar. It was established that solutions automatically generated by Genetic Programming in the field of semantic inference, in this study attribute grammars, are indeed significantly harder to comprehend than manually written ones. This finding holds, regardless of whether comprehension correctness, i.e., how many attribute grammars were correctly comprehended, or comprehension efficiency is considered, i.e., how quickly attribute grammars were correctly comprehended.

Hybrid offline programming method for robotic welding systems

• Current automatic programming approaches for robotic manufacturing systems have been reviewed. • A hybrid offline programming method that combines CAD-based and vision-based offline approaches is proposed. • The vision-based and CAD-based activities of the proposed programming method with the support techniques are presented. • Vision & CAD interactive activities are proposed to identify the workplace, detect the deviation, and compensated the path. Offline programming is an intuitive and automatic programming generation technique that does not use real robotic systems, thus greatly decreasing the downtime required for system programming, and resulting in enormous savings in terms of labor costs. Currently, offline programming can be generally categorized into computer-aided-design-based (CAD-based) and vision-based approaches; these two types of offline programming approaches have been widely applied in robotic welding systems. However, owing to the highly complex and diverse workpieces needed in the shipbuilding industry, neither of the aforementioned offline programming approaches can fully support the automatic generation of welding programs. In this paper, a hybrid offline programming method systematically combining CAD-based, vision-based, and vision & CAD interactive activities is proposed to overcome the limitations of current automatic program generation methods for robotic welding systems. In the vision-based activities, the positions of the workpieces are obtained by using geometrical features gathered from the workpieces’ images, whereas in the CAD-based activities, the welding tasks are assigned to different mobile components of the welding torch; then, their welding paths are planned according to the workpieces’ CAD models. The vision & CAD interactive activities enable the mapping between the point cloud of a workpiece and its CAD model, so that the deviations caused by assembly errors can be detected and path compensation data can be determined. The effectiveness of the proposed hybrid offline programming method is demonstrated by integrating it into a subassembly welding robotic system. The experimental results indicate that this method can significantly improve the efficiency, accuracy, and flexibility of the robotic welding system.

An Approach to Automated Programming of Industrial Robots Based on Graphic Data

Abstract The article contains the design and implementation of a system for automatic program generation for an industrial robot, based on the graphical input recorded by camera. The content is divided into two parts. The first section deals with processing the visual data acquired from an external source to appropriate form, using edge identification and transformation from raster to vector image. The second part deals with the automatic program generation for industrial robot. The article describes different possible approaches to solution. The selected way of solution was implemented in real environment in a chosen programming language. The final section of the article presents the results of experiments, together with the overall evaluation of the results.

Verification of the stationary module of the ShIPR software system for modelling experiments of the ASTRA HTGR type critical facility

Abstract Coupled neutronic and thermal hydraulic calculation codes are verified for calculating the design of modern and prospective types of nuclear reactors. This verification is done by comparing experimental and calculated results for stationary and transient conditions. This paper presents ShIPR (Shell of Intelligent Package for Reactor) Integrated Development Environment with automatic generation of head programs based on the chain of computational modules. The aim of this study is to find the reason of a discrepancy in the modelling of sub-critical states that was found in previous work. The comparison of ShIPR stationary module with Monte-Carlo code (MCU) and experimental results on ASTRA HTGR critical facility was presented in the paper. To compare the detector readings and MCU calculation with the ShIPR module, the interpolation cross-section procedure was performed. This procedure allows simulating 235U fission reaction rates (detector readings) in the complicate, annular core, using the micro-cross sections prepared by the cell-lattice code. We found that the calculation accuracy of stationary ShIPR module is on an acceptable level but the macro constants for control rods need to be prepared independently, given with surroundings.

Fuzzing channel-based concurrency runtimes using types and effects

Modern programming languages support concurrent programming based on channels and processes. Channels enable synchronous and asynchronous message-passing between independent light-weight processes making it easy to express common concurrency patterns. The implementation of channels and processes in compilers and language runtimes is a difficult task that relies heavily on traditional and error-prone low-level concurrency primitives, raising concerns about correctness and reliability. In this paper, we present an automatic program generation technique to test such programming language implementations. We define a type and effect system for programs that communicate over channels and where every execution is guaranteed to eventually terminate. We can generate and run such programs, and if a program fails to terminate, we have found a bug in the programming language implementation. We implement such an automatic program generator and apply it to Go, Kotlin, Crystal, and Flix. We find two new bugs in Flix, and reproduce two bugs; one in Crystal and one in Kotlin.

Research and Application of Machine Learning in Automatic Program Generation

With the development of artificial intelligence, machine learning has been applied in more and more domains. In order to improve the quality and efficiency of software, automatic program generation is becoming a research hotspot. In recent years, machine learning has also been gradually applied in automatic program generation. Decision trees, language models, and cyclic neural networks have been applied in code generation, code completion and code knowledge mining. The efficiency of software development has been improved to a certain extent using machine learning. Aimed at the automatic program generation, this paper analyzes and summarizes the models of machine learning, the modifications involved in the models and the application effects. The research direction is discussed from the aspects of programmer behavior and automatic program generation of machine learning.

An efficient memetic genetic programming framework for symbolic regression

Symbolic regression is one of the most common applications of genetic programming (GP), which is a popular evolutionary algorithm in automatic computer program generation. Despite existing success of GP on symbolic regression, the accuracy and efficiency of GP can still be improved especially on complicated symbolic regression problems, enabling GP to be applied to more fields. This paper proposes a novel memetic GP framework to improve the accuracy and search efficiency of GP on complicated symbolic regression problems. The proposed framework consists of two components: feature construction and feature combination. The first component focuses on constructing diverse features. The second component aims to filter redundant features and linearly combines these independent features. The first component (feature construction) focuses on constructing polynomial features derived from polynomial functions, and evolves features by a GP solver. In addition, a gradient-based nonlinear least squares algorithm named Levenberg-Marquardt (LM) is embedded in the second component (feature combination) to locally adjust the weights of independent features. A filtering mechanism is put forward to discard redundant features in the second component. Hence, the polynomial features and evolved features can work together in the framework to improve the performance of GP. Experimental results demonstrate that the proposed framework offers enhanced performance compared with several state-of-the-art algorithms in terms of accuracy and search efficiency on nine benchmark regression problems and three real-world regression problems. In this study, a novel memetic genetic programming framework is proposed to improve the performance of GP on symbolic regression. Experimental results demonstrate that the proposed framework can improve the accuracy and search efficiency of GP on complicated symbolic regression problems compared with four state-of-the-art algorithms.

A Preliminary Study of Knowledge Transfer in Multi-Classification Using Gene Expression Programming.

Gene Expression Programming (GEP), a variant of Genetic Programming (GP), is a well established technique for automatic generation of computer programs. Due to the flexible representation, GEP has long been concerned as a classification algorithm for various applications. Whereas, GEP cannot be extended to multi-classification directly, and thus is only capable of treating an M-classification task as M separate binary classifications without considering the inter-relationship among classes. Consequently, GEP-based multi-classifier may suffer from output conflict of various class labels, and the underlying conflict can probably lead to the degraded performance in multi-classification. This paper employs evolutionary multitasking optimization paradigm in an existing GEP-based multi-classification framework, so as to alleviate the output conflict of each separate binary GEP classifier. Therefore, several knowledge transfer strategies are implemented to enable the interation among the population of each separate binary task. Experimental results on 10 high-dimensional datasets indicate that knowledge transfer among separate binary classifiers can enhance multi-classification performance within the same computational budget.

Graphical Flow-based Spark Programming

Increased sensing data in the context of the Internet of Things (IoT) necessitates data analytics. It is challenging to write applications for Big Data systems due to complex, highly parallel software frameworks and systems. The inherent complexity in programming Big Data applications is also due to the presence of a wide range of target frameworks, with different data abstractions and APIs. The paper aims to reduce this complexity and its ensued learning curve by enabling domain experts, that are not necessarily skilled Big Data programmers, to develop data analytics applications via domain-specific graphical tools. The approach follows the flow-based programming paradigm used in IoT mashup tools. The paper contributes to these aspects by (i) providing a thorough analysis and classification of the widely used Spark framework and selecting suitable data abstractions and APIs for use in a graphical flow-based programming paradigm and (ii) devising a novel, generic approach for programming Spark from graphical flows that comprises early-stage validation and code generation of Spark applications. Use cases for Spark have been prototyped and evaluated to demonstrate code-abstraction, automatic data abstraction interconversion and automatic generation of target Spark programs, which are the keys to lower the complexity and its ensued learning curve involved in the development of Big Data applications.

Verification of the stationary module of the ShIPR software system for modelling experiments of the ASTRA HTGR type critical facility

Abstract Coupled neutronic and thermal hydraulic calculation codes are verified for calculating the design of modern and prospective types of nuclear reactors. This verification is done by comparing experimental and calculated results for stationary and transient conditions. This paper presents ShIPR (Shell of Intelligent Package for Reactor) Integrated Development Environment with automatic generation of head programs based on the chain of computational modules. The aim of this study is to find the reason of a discrepancy in the modelling of sub-critical states that was found in previous work. The comparison of ShIPR stationary module with Monte-Carlo code (MCU) and experimental results on ASTRA HTGR critical facility was presented in the paper. To compare the detector readings and MCU calculation with the ShIPR module, the interpolation cross-section procedure was performed. This procedure allows simulating 235U fission reaction rates (detector readings) in the complicate, annular core, using the micro-cross sections prepared by the cell-lattice code. We found that the calculation accuracy of stationary ShIPR module is on an acceptable level but the macro constants for control rods need to be prepared independently, given with surroundings.

Automatic Generation of Programs for Data Tables with Batch Least Square Mamdani Inference Systems: Application in the AWG Table

Automatic Generation of Programs for Data Tables with Batch Least Square Mamdani Inference Systems: Application in the AWG Table

AUTOMATIC GENERATION OF THE PLC PROGRAMS FOR THE SEQUENTIAL CONTROL OF PNEUMATIC ACTUATORS

Nowadays, programmable logic controllers (PLC) are widely used in many automated systems, especially for the control of various actuators. The most common PLC programming is performed by either using a ladder diagram or a structured text. The paper presents the automatic generation of PLC programs for the purpose of sequentially controlling pneumatic actuators. In this paper, the pneumatic actuators are supplied and controlled by 5/2-way as well as 5/3-way bistable pneumatic valves with electric activation. The valve type depends on the number of positions in which the actuator should come, and the position sensors are used for detecting its movement. The characteristic encoding of the movement of actuators, position sensors and control commands is performed. The advantages of the automatic generation of the PLC commands and the entire program described in this paper are illustrated in a real example.

ABB Robotic Arm Offline Programming System

The offline programming technology of industrial robots is more and more widely used because it can effectively improve work efficiency. A DXF drawing file is a file generated by CAD that can serve as a bridge for data exchange with other software. Through experiments, this paper combines CAD-generated DXF drawing files with ABB robotic arm to realize data extraction of CAD drawings, and automatic generation and storage of robotic arm running programs, which are imported into the robot arm, so that the robot arm can be made according to CAD. The trajectory on the drawing performs the welding function. The simulation of path planning was carried out under the Robot studio software developed by ABB Robotics to meet the experimental requirements. The realization of this off-line programming system achieves the following two purposes. One is to assist non-control professional teaching, to help understand the simulation and actual production and processing of industrial robot arm; the second is to improve the efficiency of industrial robot arm programming and realize full automation programming.

A Survey of Genetic Programming and Its Applications

Genetic Programming (GP) is an intelligence technique whereby computer programs are encoded as a set of genes which are evolved utilizing a Genetic Algorithm (GA). In other words, the GP employs novel optimization techniques to modify computer programs; imitating the way humans develop programs by progressively re-writing them for solving problems automatically. Trial programs are frequently altered in the search for obtaining superior solutions due to the base is GA. These are evolutionary search techniques inspired by biological evolution such as mutation, reproduction, natural selection, recombination, and survival of the fittest. The power of GAs is being represented by an advancing range of applications; vector processing, quantum computing, VLSI circuit layout, and so on. But one of the most significant uses of GAs is the automatic generation of programs. Technically, the GP solves problems automatically without having to tell the computer specifically how to process it. To meet this requirement, the GP utilizes GAs to a “population” of trial programs, traditionally encoded in memory as tree-structures. Trial programs are estimated using a “fitness function” and the suited solutions picked for re-evaluation and modification such that this sequence is replicated until a “correct” program is generated. GP has represented its power by modifying a simple program for categorizing news stories, executing optical character recognition, medical signal filters, and for target identification, etc. This paper reviews existing literature regarding the GPs and their applications in different scientific fields and aims to provide an easy understanding of various types of GPs for beginners.

Methodology for Railway Automation Study and Automatic Generation of PLC Programs

This paper presents an original approach to standardization of Electrical Traction's work of railway. This approach was developed within an industrial thesis, financed by the SNCF (French acronym for National Society of French Railways) in association with the CReSTIC (Research Center in Information and Communication Science and Technologies), laboratory of university of Reims Champagne-Ardenne. It consists of two axes of improvement. The first one concerns the standardization and automatic generation of deliverables (wiring diagrams, PLC programs...) established initially by the systems engineers. Therefore these latter will avoid repetitive tasks which can lead to mental underload and then, focus their attention on cognitive tasks. The second axe of the proposed methodology consists in integrating a robust filter based on the use of safety constraints in a PLC program, so that the safety of the system can be ensured whatever the functional program. Thus, this solution guarantees safety without changing the know-how of the systems engineers. This paper presents only the first axe of the proposed methodology.