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Relevant bibliographies by topics / Genetic programming (Computer science)

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Academic literature on the topic 'Genetic programming (Computer science)'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 July 2024

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Journal articles on the topic "Genetic programming (Computer science)"

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Toulouse. "Automatic Quantum Computer Programming: A Genetic Programming Approach." Genetic Programming and Evolvable Machines 7, no. 1 (2006): 125. http://dx.doi.org/10.1007/s10710-005-4866-8.

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Toulouse, Michel. "Automatic Quantum Computer Programming: A Genetic Programming Approach." Genetic Programming and Evolvable Machines 7, no. 1 (2006): 125–26. http://dx.doi.org/10.1007/s10710-006-4866-3.

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Gielen, C. "Genetic programming." Neurocomputing 6, no. 1 (1994): 120–22. http://dx.doi.org/10.1016/0925-2312(94)90038-8.

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Ciesielski, Vic. "Linear genetic programming." Genetic Programming and Evolvable Machines 9, no. 1 (2007): 105–6. http://dx.doi.org/10.1007/s10710-007-9036-8.

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O'Neill, Michael, and Anthony Brabazon. "Recent Patents on Genetic Programming." Recent Patents on Computer Science 2, no. 1 (2009): 43–49. http://dx.doi.org/10.2174/1874479600902010043.

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O'Neill, Michael, and Anthony Brabazon. "Recent Patents on Genetic Programming." Recent Patents on Computer Science 2, no. 1 (2010): 43–49. http://dx.doi.org/10.2174/1874479610902010043.

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Langdon, W. B., and W. Banzhaf. "Repeated patterns in genetic programming." Natural Computing 7, no. 4 (2007): 589–613. http://dx.doi.org/10.1007/s11047-007-9038-8.

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Françoso Dal Piccol Sotto, Léo, Paul Kaufmann, Timothy Atkinson, Roman Kalkreuth, and Márcio Porto Basgalupp. "Graph representations in genetic programming." Genetic Programming and Evolvable Machines 22, no. 4 (2021): 607–36. http://dx.doi.org/10.1007/s10710-021-09413-9.

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AbstractGraph representations promise several desirable properties for genetic programming (GP); multiple-output programs, natural representations of code reuse and, in many cases, an innate mechanism for neutral drift. Each graph GP technique provides a program representation, genetic operators and overarching evolutionary algorithm. This makes it difficult to identify the individual causes of empirical differences, both between these methods and in comparison to traditional GP. In this work, we empirically study the behaviour of Cartesian genetic programming (CGP), linear genetic programming (LGP), evolving graphs by graph programming and traditional GP. By fixing some aspects of the configurations, we study the performance of each graph GP method and GP in combination with three different EAs: generational, steady-state and $$(1+\lambda )$$ ( 1 + λ ) . In general, we find that the best choice of representation, genetic operator and evolutionary algorithm depends on the problem domain. Further, we find that graph GP methods can increase search performance on complex real-world regression problems and, particularly in combination with the ($$1 + \lambda$$ 1 + λ ) EA, are significantly better on digital circuit synthesis tasks. We further show that the reuse of intermediate results by tuning LGP’s number of registers and CGP’s levels back parameter is of utmost importance and contributes significantly to better convergence of an optimization algorithm when solving complex problems that benefit from code reuse.

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O’Neill, Michael, Leonardo Vanneschi, Steven Gustafson, and Wolfgang Banzhaf. "Open issues in genetic programming." Genetic Programming and Evolvable Machines 11, no. 3-4 (2010): 339–63. http://dx.doi.org/10.1007/s10710-010-9113-2.

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Giot, Romain, and Christophe Rosenberger. "Genetic programming for multibiometrics." Expert Systems with Applications 39, no. 2 (2012): 1837–47. http://dx.doi.org/10.1016/j.eswa.2011.08.066.

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Dissertations / Theses on the topic "Genetic programming (Computer science)"

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Fine, Steven B. "Extensions to behavioral genetic programming." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112846.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (page 55).<br>In this work I introduce genetic programming [5] as a general technique to produce programs with arbitrary behavior. I discuss genetic programming and its application the task of symbolic regression. I introduce behavioral genetic programming [6] as an extension to genetic programming and explore various extensions to it. The codebase that I build is made sufficiently flexible to easily accommodate future adaptions to the behavioral genetic programming methodology. I test the performance of the implementation of behavioral genetic programming along with several extensions.<br>by Steven B. Fine.<br>M. Eng.

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Gustafson, Steven Matt. "An analysis of diversity in genetic programming." Thesis, University of Nottingham, 2004. http://eprints.nottingham.ac.uk/10057/.

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Genetic programming is a metaheuristic search method that uses a population of variable-length computer programs and a search strategy based on biological evolution. The idea of automatic programming has long been a goal of artificial intelligence, and genetic programming presents an intuitive method for automatically evolving programs. However, this method is not without some potential drawbacks. Search using procedural representations can be complex and inefficient. In addition, variable sized solutions can become unnecessarily large and difficult to interpret. The goal of this thesis is to understand the dynamics of genetic programming that encourages efficient and effective search. Toward this goal, the research focuses on an important property of genetic programming search: the population. The population is related to many key aspects of the genetic programming algorithm. In this programme of research, diversity is used to describe and analyse populations and their effect on search. A series of empirical investigations are carried out to better understand the genetic programming algorithm. The research begins by studying the relationship between diversity and search. The effect of increased population diversity and a metaphor of search are then examined. This is followed by an investigation into the phenomenon of increased solution size and problem difficulty. The research concludes by examining the role of diverse individuals, particularly the ability of diverse individuals to affect the search process and ways of improving the genetic programming algorithm. This thesis makes the following contributions: (1) An analysis shows the complexity of the issues of diversity and the relationship between diversity and fitness, (2) The genetic programming search process is characterised by using the concept of genetic lineages and the sampling of structures and behaviours, (3) A causal model of the varied rates of solution size increase is presented, (4) A new, tunable problem demonstrates the contribution of different population members during search, and (5) An island model is proposed to improve the search by speciating dissimilar individuals into better-suited environments. Currently, genetic programming is applied to a wide range of problems under many varied contexts. From artificial intelligence to operations research, the results presented in this thesis will benefit population-based search methods, methods based on the concepts of evolution and search methods using variable-length representations.

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Yu, Chris 1981. "Characterizing function inlining with genetic programming." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/33392.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.<br>Includes bibliographical references (leaves 74-75).<br>Function inlining is a compiler optimization where the function call is replaced by the code from the function itself. Using a form of machine learning called genetic programming, this thesis examines which factors are important in determining which function calls to inline to maximize performance. A number of different heuristics are generated for inlining decisions in the Trimaran compiler, which improve on performance from the current default inlining heuristic. Also, trends in function inlining are examined over the thousands of compilation runs that are completed.<br>by Chris Yu.<br>M.Eng.

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Paterson, Norman R. "Genetic programming with context-sensitive grammars." Thesis, University of St Andrews, 2003. http://hdl.handle.net/10023/14984.

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This thesis presents Genetic Algorithm for Deriving Software (Gads), a new technique for genetic programming. Gads combines a conventional genetic algorithm with a context-sensitive grammar. The key to Gads is the onto genic mapping, which converts a genome from an array of integers to a correctly typed program in the phenotype language defined by the grammar. A new type of grammar, the reflective attribute grammar (rag), is introduced. The rag is an extension of the conventional attribute grammar, which is designed to produce valid sentences, not to recognize or parse them. Together, Gads and rags provide a scalable solution for evolving type-correct software in independently-chosen context-sensitive languages. The statistics of performance comparison is investigated. A method for representing a set of genetic programming systems or problems on a cladogram is presented. A method for comparing genetic programming systems or problems on a single rational scale is proposed.

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August, Riley. "Applying genetic programming to scripted mobile robotics." Thesis, University of Ottawa (Canada), 2009. http://hdl.handle.net/10393/28474.

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In this thesis, we develop a new language for genetic programming, specifically designed for high-level controller scripting on mobile robots. We then test this language against previous conventions on the Robots Everywhere Antbot platform. We develop a genetic programming framework using Python and the new language, to create corridor-following programs in a simple simulation. Using this framework, we conduct a variety of experiments to find good parameters and techniques that apply to this new language, which can evolve the best controllers. Our results suggest that hierarchical GP using a measure of elitism is likely the best solution, and that the new language is very capable of evolving solutions with a high degree of robustness and generality.

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Bannister, Christian. "Automated development of clinical prediction models using genetic programming." Thesis, Cardiff University, 2015. http://orca.cf.ac.uk/90825/.

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Genetic programming is an Evolutionary Computing technique, inspired by biological evolution, capable of discovering complex non-linear patterns in large datasets. Genetic programming is a general methodology, the specific implementation of which requires development of several different specific elements such as problem representation, fitness, selection and genetic variation. Despite the potential advantages of genetic programming over standard statistical methods, its applications to survival analysis are at best rare, primarily because of the difficulty in handling censored data. The aim of this work was to develop a genetic programming approach for survival analysis and demonstrate its utility for the automatic development of clinical prediction models using cardiovascular disease as a case study. We developed a tree-based untyped steady-state genetic programming approach for censored longitudinal data, comparing its performance to the de facto statistical method—Cox regression—in the development of clinical prediction models for the prediction of future cardiovascular events in patients with symptomatic and asymptomatic cardiovascular disease, using large observational datasets. We also used genetic programming to examine the prognostic significance of different risk factors together with their non-linear combinations for the prognosis of health outcomes in cardiovascular disease. These experiments showed that Cox regression and the developed steady-state genetic programming approach produced similar results when evaluated in common validation datasets. Despite slight relative differences, both approaches demonstrated an acceptable level of discriminative and calibration at a range of times points. Whilst the application of genetic programming did not provide more accurate representations of factors that predict the risk of both symptomatic and asymptomatic cardiovascular disease when compared with existing methods, genetic programming did offer comparable performance. Despite generally comparable performance, albeit in slight favour of the Cox model, the predictors selected for representing their relationships with the outcome were quite different and, on average, the models developed using genetic programming used considerably fewer predictors. The results of the genetic programming confirm the prognostic significance of a small number of the most highly associated predictors in the Cox modelling; age, previous atherosclerosis, and albumin for secondary prevention; age, recorded diagnosis of ’other’ cardiovascular disease, and ethnicity for primary prevention in patients with type 2 diabetes. When considered as a whole, genetic programming did not produce better performing clinical prediction models, rather it utilised fewer predictors, most of which were the predictors that Cox regression estimated be most strongly associated with the outcome, whilst achieving comparable performance. This suggests that genetic programming may better represent the potentially non-linear relationship of (a smaller subset of) the strongest predictors. To our knowledge, this work is the first study to develop a genetic programming approach for censored longitudinal data and assess its value for clinical prediction in comparison with the well-known and widely applied Cox regression technique. Using empirical data this work has demonstrated that clinical prediction models developed by steady-state genetic programming have predictive ability comparable to those developed using Cox regression. The genetic programming models were more complex and thus more difficult to validate by domain experts, however these models were developed in an automated fashion, using fewer input variables, without the need for domain specific knowledge and expertise required to appropriately perform survival analysis. This work has demonstrated the strong potential of genetic programming as a methodology for automated development of clinical prediction models for diagnostic and prognostic purposes in the presence of censored data. This work compared untuned genetic programming models that were developed in an automated fashion with highly tuned Cox regression models that was developed in a very involved manner that required a certain amount of clinical and statistical expertise. Whilst the highly tuned Cox regression models performed slightly better in validation data, the performance of the automatically generated genetic programming models were generally comparable. The comparable performance demonstrates the utility of genetic programming for clinical prediction modelling and prognostic research, where the primary goal is accurate prediction. In aetiological research, where the primary goal is to examine the relative strength of association between risk factors and the outcome, then Cox regression and its variants remain as the de facto approach.

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Steinhoff, Robert J. "A Performance Comparison of Tree-Based Genetic Programming versus Stack-Based Genetic Programming versus Stack-Based Genetic Programming Using the Java Virtual Machine." NSUWorks, 2000. http://nsuworks.nova.edu/gscis_etd/859.

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Stack-based genetic programming uses variable length, linear programs executing on a virtual stack machine. This concept was proposed and evaluated by Timothy Perkis. The Java programming language uses a stack-based virtual machine to perform operations. This paper examined the possibility of performing stack-based genetic programming directly using the stack on the Java virtual machine. The problems of combining stack based genetic programming with the Java virtual machine stack were explored. Excessive runtime delay on bytecode verification of the chromosome carrying classes undergoing fitness evaluation was identified. Another problem is that the Java virtual machine stack must be tightly controlled and cannot have illegal operands. Direct comparison of stack-based genetic programming on the Java virtual machine to common tree-based genetic programming was not performed due to discovered flaws in the architecture. A practical model to implement stack-based genetic programming on the Java virtual machine using a class bytecode assembler was proposed. This model combines the GPsys genetic programming system with the JAS bytecode assembler resulting in an architecture called GPsys-JAS. A further recommendation to compare stack-based genetic programming on the Java virtual machine against stack-based genetic programming using the Java Stack class was suggested.

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Hyde, Matthew. "A genetic programming hyper-heuristic approach to automated packing." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/11625/.

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This thesis presents a programme of research which investigated a genetic programming hyper-heuristic methodology to automate the heuristic design process for one, two and three dimensional packing problems. Traditionally, heuristic search methodologies operate on a space of potential solutions to a problem. In contrast, a hyper-heuristic is a heuristic which searches a space of heuristics, rather than a solution space directly. The majority of hyper-heuristic research papers, so far, have involved selecting a heuristic, or sequence of heuristics, from a set pre-defined by the practitioner. Less well studied are hyper-heuristics which can create new heuristics, from a set of potential components. This thesis presents a genetic programming hyper-heuristic which makes it possible to automatically generate heuristics for a wide variety of packing problems. The genetic programming algorithm creates heuristics by intelligently combining components. The evolved heuristics are shown to be highly competitive with human created heuristics. The methodology is first applied to one dimensional bin packing, where the evolved heuristics are analysed to determine their quality, specialisation, robustness, and scalability. Importantly, it is shown that these heuristics are able to be reused on unseen problems. The methodology is then applied to the two dimensional packing problem to determine if automatic heuristic generation is possible for this domain. The three dimensional bin packing and knapsack problems are then addressed. It is shown that the genetic programming hyper-heuristic methodology can evolve human competitive heuristics, for the one, two, and three dimensional cases of both of these problems. No change of parameters or code is required between runs. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains.

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Pinder, Robert William 1977. "Applications of genetic programming to parallel system optimization." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86507.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.<br>Includes bibliographical references (p. 81-84).<br>by Robert William Pinder.<br>M.Eng.

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Lotz, Marco. "Modelling of process systems with Genetic Programming /." Thesis, Link to the online version, 2006. http://hdl.handle.net/10019/570.

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Books on the topic "Genetic programming (Computer science)"

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Spector, Lee C. Automatic quantum computer programming: A genetic programming approach. Kluwer Academic Publishers, 2004.

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service), SpringerLink (Online, ed. Cartesian Genetic Programming. Springer-Verlag Berlin Heidelberg, 2011.

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EuroGP 2007 (2007 Valencia, Spain). Genetic programming: Proceedings. Springer, 2007.

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Riolo, Rick. Genetic Programming Theory and Practice. Springer US, 2003.

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Branko, Souček, and IRIS Group, eds. Dynamic, genetic, and chaotic programming: The sixth-generation. Wiley, 1992.

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O'Reilly, Una-May. Genetic programming theory and practice II. Springer Science+Business Media, 2005.

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Riolo, Rick. Genetic Programming Theory and Practice IX. Springer Science+Business Media, LLC, 2011.

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R, Koza John, ed. Genetic programming IV: Routine human-competitive machine intelligence. Kluwer Academic Publishers, 2003.

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Koza, John R. Genetic programming: On the programming of computers by means of natural selection. MIT Press, 1992.

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Yoshihiko, Hasegawa, and Paul Topon Kumar, eds. Applied genetic programming and machine learning. Taylor & Francis, 2009.

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Book chapters on the topic "Genetic programming (Computer science)"

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Rodrigues, Nuno M., João E. Batista, and Sara Silva. "Ensemble Genetic Programming." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44094-7_10.

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Izzo, Dario, Francesco Biscani, and Alessio Mereta. "Differentiable Genetic Programming." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55696-3_3.

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Miller, Julian F., and Peter Thomson. "Cartesian Genetic Programming." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-540-46239-2_9.

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Marchetti, Francesco, and Edmondo Minisci. "Inclusive Genetic Programming." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72812-0_4.

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Rodriguez-Coayahuitl, Lino, Alicia Morales-Reyes, and Hugo Jair Escalante. "Convolutional Genetic Programming." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21077-9_5.

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Miralavy, Iliya, and Wolfgang Banzhaf. "Spatial Genetic Programming." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-29573-7_17.

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Wilson, Garnett, and Wolfgang Banzhaf. "A Comparison of Cartesian Genetic Programming and Linear Genetic Programming." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-78671-9_16.

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Cotillon, Alban, Philip Valencia, and Raja Jurdak. "Android Genetic Programming Framework." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29139-5_2.

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Curry, Robert, and Malcolm I. Heywood. "One-Class Genetic Programming." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01181-8_1.

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Moraglio, Alberto, Krzysztof Krawiec, and Colin G. Johnson. "Geometric Semantic Genetic Programming." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32937-1_3.

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Conference papers on the topic "Genetic programming (Computer science)"

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A, Krishna Bhargava, Deepak Kumar Sinha, and Garima Sinha. "Network Optimization Using Genetic Programming." In 2023 International Conference on Computer Science and Emerging Technologies (CSET). IEEE, 2023. http://dx.doi.org/10.1109/cset58993.2023.10346779.

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Zhang, Jian, and Chaohui Zhang. "Power System Load Modeling Based on Genetic Programming." In International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2014). Atlantis Press, 2014. http://dx.doi.org/10.2991/lemcs-14.2014.31.

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Afzal, Wasif, and Richard Torkar. "Suitability of Genetic Programming for Software Reliability Growth Modeling." In 2008 International Symposium on Computer Science and its Applications (CSA). IEEE, 2008. http://dx.doi.org/10.1109/csa.2008.13.

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Pebriadi, Muhammad Syahid, Vektor Dewanto, Wisnu Ananta Kusuma, Farit Mochamad Afendi, and Rudi Heryanto. "Learning similarity functions for binary strings via genetic programming." In 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS). IEEE, 2016. http://dx.doi.org/10.1109/icacsis.2016.7872773.

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Montoya, Nelson, Cynthia B. Perez, Luis A. Castro, and Eddie Clemente. "Exploring Optimal Parameter Combinations for Genetic Programming in Robot Trajectory Generation." In 2023 Mexican International Conference on Computer Science (ENC). IEEE, 2023. http://dx.doi.org/10.1109/enc60556.2023.10508697.

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Yu, Yang, Hui Ma, and Mengjie Zhang. "A genetic programming approach to distributed execution of data-intensive web service compositions." In ACSW '16: Australasian Computer Science Week. ACM, 2016. http://dx.doi.org/10.1145/2843043.2843046.

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Maher, Rami A., and Mohammad J. Mohammad. "Identification of Nonlinear Discrete Dynamic Systems Using Enhanced Genetic Programming." In 2017 European Conference on Electrical Engineering and Computer Science (EECS). IEEE, 2017. http://dx.doi.org/10.1109/eecs.2017.49.

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Azimlu, Fateme, Shahryar Rahnamayan, Masoud Makrehchi, and Naveen Kalra. "Comparing Genetic Programming with Other Data Mining Techniques on Prediction Models." In 2019 14th International Conference on Computer Science & Education (ICCSE). IEEE, 2019. http://dx.doi.org/10.1109/iccse.2019.8845381.

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Erdem, Mehmet Bilgehan, Zekiye Erdem, and Shahryar Rahnamayan. "Diabetes Mellitus Prediction Using Multi-objective Genetic Programming and Majority Voting." In 2019 14th International Conference on Computer Science & Education (ICCSE). IEEE, 2019. http://dx.doi.org/10.1109/iccse.2019.8845515.

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Koohestani, Behrooz, and Riccardo Poli. "On the application of Genetic Programming to the envelope reduction problem." In 2012 4th Computer Science and Electronic Engineering Conference (CEEC). IEEE, 2012. http://dx.doi.org/10.1109/ceec.2012.6375378.

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Reports on the topic "Genetic programming (Computer science)"

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Anderson, Loren James, and Marion Kei Davis. Functional Programming in Computer Science. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1237221.

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Proskura, Svitlana L., and Svitlana H. Lytvynova. The approaches to Web-based education of computer science bachelors in higher education institutions. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3892.

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The problem of organizing of Web-based education of bachelors, and the bachelors of computer science in particular, is relevant for higher education institutions. The IT industry puts forward new requirements for future IT professionals training. This, in its turn, requires the educational process modernization: content specification, updating of forms, methods and means of training to meet the demands of socio-economic development of the society in general and bachelors of computer science in particular. The article analyzes and clarifies the notion of Web-based education of bachelors; as well as a line of approaches, such as approaches to the organization of Web-based learning for A La Carte, Station Rotation, Lab Rotation, Individual Rotation, Flipped Learning scenario; the necessity of cloud computing and virtual classroom use as a component of Web-based learning is substantiated. It is established that with the advent of a large number of cloud-based services, augmented and virtual realities, new conditions are created for the development of skills to work with innovative systems. It is noted that the implementation of the approaches to the organization of student Web-based education is carried out on international level, in such projects as Erasmus+ “Curriculum for Blended Learning” and “Blended learning courses for teacher educators between Asia and Europe”. The article features the results of programming students survey on the use of Web-based technologies while learning, namely the results of a new approach to learning organization according to the formula – traditional (30%), distance (50%) and project (20%) training.

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Hlushak, Oksana M., Volodymyr V. Proshkin, and Oksana S. Lytvyn. Using the e-learning course “Analytic Geometry” in the process of training students majoring in Computer Science and Information Technology. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3268.

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As a result of literature analysis the expediency of free access of bachelors majoring in Computer Sciences and Information Technologies to modern information educational resources, in particular, e-learning courses in the process of studying mathematical disciplines is substantiated. It was established that the e-learning course is a complex of teaching materials and educational services created for the organization of individual and group training using information and communication technologies. Based on the outlined possibilities of applying the e-learning course, as well as its didactic functions, the structure of the certified e-learning course “Analytic Geometry” based on the Moodle platform was developed and described. Features of application of cloud-oriented resources are considered: Desmos, Geogebra, Wolfram|Alpha, Sage in the study of the discipline “Analytic Geometry”. The results of the pedagogical experiment on the basis of Borys Grinchenko Kyiv University and A. S. Makarenko Sumy State Pedagogical University are presented. The experiment was conducted to verify the effectiveness of the implementation of the e-learning course “Analytic Geometry”. Using the Pearson criterion it is proved that there are significant differences in the level of mathematical preparation of experimental and control group of students. The prospect of further scientific research is outlined through the effectiveness of the use of e-learning courses for the improvement of additional professional competences of students majoring in Computer Sciences and Information Technologies (specialization “Programming”, “Internet of Things”).

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Tkachuk, Viktoriia V., Vadym P. Shchokin, and Vitaliy V. Tron. The Model of Use of Mobile Information and Communication Technologies in Learning Computer Sciences to Future Professionals in Engineering Pedagogy. [б. в.], 2018. http://dx.doi.org/10.31812/123456789/2668.

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Research goal: the research is aimed at developing a model of use of mobile ICT in learning Computer Sciences to future professionals in Engineering Pedagogy. Object of research is the model of use of mobile ICT in learning Computer Sciences to future professionals in Engineering Pedagogy. Results of the research: the developed model of use of mobile ICT as tools of learning Computer Sciences to future professionals in Engineering Pedagogy is based on the competency-based, person-centered and systemic approaches considering principles of vocational education, general didactic principles, principles of Computer Science learning, and principles of mobile learning. It also takes into account current conditions and trends of mobile ICT development. The model comprises four blocks: the purpose-oriented block, the content-technological block, the diagnostic block and the result-oriented block. According to the model, the learning content of Computer Sciences consists of 5 main units: 1) Fundamentals of Computer Science; 2) Architecture of Modern Computers; 3) Fundamentals of Algorithmization and Programming; 4) Software of Computing Systems; 5) Computer Technologies in the Professional Activity of Engineer-pedagogues.

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Markova, Oksana M., Serhiy O. Semerikov, Andrii M. Striuk, Hanna M. Shalatska, Pavlo P. Nechypurenko, and Vitaliy V. Tron. Implementation of cloud service models in training of future information technology specialists. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3270.

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Leading research directions are defined on the basis of self-analysis of the study results on the use of cloud technologies in training by employees of joint research laboratory “Сloud technologies in education” of Kryvyi Rih National University and Institute of Information Technology and Learning Aids of the NAES of Ukraine in 2009-2018: cloud learning technologies, cloud technologies of blended learning, cloud-oriented learning environments, cloud-oriented methodological systems of training, the provision of cloud-based educational services. The ways of implementation SaaS, PaaS, IaaS cloud services models which are appropriate to use in the process of studying the academic disciplines of the cycles of mathematical, natural science and professional and practical training of future specialists in information technology are shown, based on the example of software engineering, computer science and computer engineering. The most significant advantages of using cloud technologies in training of future information technology specialists are definite, namely, the possibility of using modern parallel programming tools as the basis of cloud technologies. Conclusions are drawn; the direction of further research is indicated: designing a cloud-oriented learning environment for future specialists in computer engineering, identifying trends in the development of cloud technologies in the professional training and retraining of information technology specialists, developing a methodology for building the research competencies of future software engineering specialists by using cloud technologies.

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Modlo, Yevhenii O., Serhiy O. Semerikov, Pavlo P. Nechypurenko, Stanislav L. Bondarevskyi, Olena M. Bondarevska, and Stanislav T. Tolmachev. The use of mobile Internet devices in the formation of ICT component of bachelors in electromechanics competency in modeling of technical objects. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3264.

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Computer simulation of technical objects and processes is one of the components of the system of professional training of a modern electromechanics engineer. It has been established that despite the fact that mobile Internet devices (MID) are actively used by electrical engineers, the methods of using them in the process of bachelor in electromechanics training is considered only in some domestic scientific studies. The article highlights the components of the methods of using MID in the formation of the ICT component of the competence of the bachelor in electromechanics in modeling of technical objects, providing for students to acquire basic knowledge in the field of Computer Science and modern ICT and skills to use programming systems, math packages, subroutine libraries, and the like. For processing tabular data, it is proposed to use various freely distributed tools that do not significantly differ in functionality, such as Google Sheets, Microsoft Excel, for processing text data – QuickEdit Text Editor, Google Docs, Microsoft Word. For 3D-modeling and viewing the design and technological documentation, the proposed comprehensive use of Autodesk tools in the training process.

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Balyk, Nadiia, Svitlana Leshchuk, and Dariia Yatsenyak. Developing a Mini Smart House model. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3741.

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The work is devoted to designing a smart home educational model. The authors analyzed the literature in the field of the Internet of Things and identified the basic requirements for the training model. It contains the following levels: command, communication, management. The authors identify the main subsystems of the training model: communication, signaling, control of lighting, temperature, filling of the garbage container, monitoring of sensor data. The proposed smart home educational model takes into account the economic indicators of resource utilization, which gives the opportunity to save on payment for their consumption. The hardware components for the implementation of the Mini Smart House were selected in the article. It uses a variety of technologies to conveniently manage it and use renewable energy to power it. The model was produced independently by students involved in the STEM project. Research includes sketching, making construction parts, sensor assembly and Arduino boards, programming in the Arduino IDE environment, testing the functioning of the system. Research includes sketching, making some parts, assembly sensor and Arduino boards, programming in the Arduino IDE environment, testing the functioning of the system. Approbation Mini Smart House researches were conducted within activity the STEM-center of Physics and Mathematics Faculty of Ternopil Volodymyr Hnatiuk National Pedagogical University, in particular during the educational process and during numerous trainings and seminars for pupils and teachers of computer science.

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Shamonia, Volodymyr H., Olena V. Semenikhina, Volodymyr V. Proshkin, Olha V. Lebid, Serhii Ya Kharchenko, and Oksana S. Lytvyn. Using the Proteus virtual environment to train future IT professionals. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3760.

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Based on literature review it was established that the use of augmented reality as an innovative technology of student training occurs in following directions: 3D image rendering; recognition and marking of real objects; interaction of a virtual object with a person in real time. The main advantages of using AR and VR in the educational process are highlighted: clarity, ability to simulate processes and phenomena, integration of educational disciplines, building an open education system, increasing motivation for learning, etc. It has been found that in the field of physical process modelling the Proteus Physics Laboratory is a popular example of augmented reality. Using the Proteus environment allows to visualize the functioning of the functional nodes of the computing system at the micro level. This is especially important for programming systems with limited resources, such as microcontrollers in the process of training future IT professionals. Experiment took place at Borys Grinchenko Kyiv University and Sumy State Pedagogical University named after A. S. Makarenko with students majoring in Computer Science (field of knowledge is Secondary Education (Informatics)). It was found that computer modelling has a positive effect on mastering the basics of microelectronics. The ways of further scientific researches for grounding, development and experimental verification of forms, methods and augmented reality, and can be used in the professional training of future IT specialists are outlined in the article.

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Striuk, Andrii M. Software engineering: first 50 years of formation and development. [б. в.], 2018. http://dx.doi.org/10.31812/123456789/2880.

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The article analyzes the main stages of software engineering (SE) development. Based on the analysis of materials from the first SE conferences (1968-1969), it was determined how the software crisis prompted scientists and practitioners to join forces to form an engineering approach to programming. Differences in professional training for SE are identified. The fundamental components of the training of future software engineers are highlighted. The evolution of approaches to the design, implementation, testing and documentation of software is considered. The system scientific, technological approaches and methods for the design and construction of computer programs are highlighted. Analysis of the historical stages of the development of SE showed that despite the universal recognition of the importance of using the mathematical apparatus of logic, automata theory and linguistics when developing software, it was created empirically without its use. The factor that led practitioners to turn to the mathematical foundations of an SE is the increasing complexity of software and the inability of empirical approaches to its development and management to cope with it. The training of software engineers highlighted the problem of the rapid obsolescence of the technological content of education, the solution of which lies in its fundamentalization through the identification of the basic foundations of the industry. It is determined that mastering the basics of computer science is the foundation of vocational training in SE.

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