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14th International Symposium on Intelligent Systems

作者： Konyrbaev, N. B. Dauitbayeva, A. O. Ostayeva, A. B. Yessirkepova, A. U. Bexeitova, A. B. Korkyt ATA Kyzylorda State Univ Aiteke Bi 29A Kyzylorda 120014 Kazakhstan

In this paper we use analytic programming method for optimal control synthesis. We developed variation version of the analytic programming to improve the search process efficiency. The method perform search over the set of the small variations of the given basic solution. Search efficiency depends on the basic solution. We give an example of optimal control synthesis for the three-dimension system with the state constraints over the set of the initial states using this method. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 14th International Symposium "Intelligent Systems".

关键词： analytic programming optimal control synthesis problem

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Variational analytic programming for synthesis of optimal control

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Procedia Computer Science 2021年 186卷 743-750页

作者： N.B. Konyrbaev A.O. Dauitbayeva A.B. Ostayeva A.U. Yessirkepova A.B. Bexeitova Korkyt ATA Kyzylorda state University Aiteke Bi 29A Kyzylorda 120014 Kazakhstan

In this paper we use analytic programming method for optimal control synthesis. We developed variation version of the analytic programming to improve the search process eﬃciency. The method perform search over the set of the small variations of the given basic solution. Search eﬃciency depends on the basic solution. We give an example of optimal control synthesis for the three-dimension system with the state constraints over the set of the initial states using this method.

关键词： analytic programming optimal control synthesis problem

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On the Applicability of Random and the Best Solution Driven Metaheuristics for analytic programming and Time Series Regression 7th

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7th Computer Science On-Line Conference (CSOC)

作者： Senkerik, Roman Viktorin, Adam Pluhacek, Michal Kadavy, Tomas Oplatkova, Zuzana Kominkova Tomas Bata Univ Zlin Fac Appl Informat Nam TG Masaryka 5555 Zlin 76001 Czech Republic

ISBN: (纸本)9783319911892

This paper provides a closer insight into applicability and performance of the hybridization of symbolic regression open framework, which is analytical programming (AP) and Differential Evolution (DE) algorithm in the task of time series regression. AP can be considered as a robust open framework for symbolic regression thanks to its usability in any programming language with arbitrary driving metaheuristic. The motivation behind this research is to explore and investigate the applicability and differences in performance of AP driven by basic canonical entirely random or best solution driven mutation strategies of DE. An experiment with four case studies has been carried out here with the several time series consisting of GBP/USD exchange rate. The differences between regression/prediction models synthesized using AP as a direct consequence of different DE strategies performances are statistically compared and briefly discussed in conclusion section of this paper.

关键词： analytic programming Differential Evolution Time series regression

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PSEUDO NEURAL NETWORKS VIA analytic programming WITH DIRECT CODING OF CONSTANT ESTIMATION 32

PSEUDO NEURAL NETWORKS VIA ANALYTIC PROGRAMMING WITH DIRECT ...

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32nd European Conference on Modelling and Simulation (ECMS)

作者： Oplatkova, Zuzana Kominkova Viktorin, Adam Senkerik, Roman Tomas Bata Univ Zlin Fac Appl Info Lat Nam TG Masaryka 5555 Zlin 76001 Czech Republic

ISBN: (纸本)9780993244070

This research deals with a novel approach to classification - pseudo neural networks (PNN). This technique was inspired in classical artificial neural networks (ANN), where a relation between inputs and outputs is based on the mathematical transfer functions and optimised numerical weights. Compared to ANN, the whole structure in PNN, i.e. the relation between inputs and output(s), is fully synthesised by evolutionary symbolic regression tool - analytic programming. Compared to previous synthesised models, the PNN in this paper were synthesised via a new approach to constant estimation inside the analytic programming - direct coding. Iris data was used for the experiments and PNN were used for the synthesis of a complex classifier for more classes. For experimentation, Differential Evolution (de/rand/1/bin) for optimisation in analytic programming (AP) was used.

关键词： Pseudo neural networks analytic programming Differential evolution

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Differential Evolution and analytic programming in the case of Trigonometric Identities Discovery 25

Differential Evolution and Analytic Programming in the case ...

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25th International Conference on Systems, Signals and Image Processing (IWSSIP)

作者： Oplatkova, Zuzana Kominkova Senkerik, Roman Viktorin, Adam Tomas Bata Univ Zlin Fac Appl Informat Nam TG Masaryka 5555 Zlin Czech Republic

The paper deals with the discovery of trigonometric identities of four functions via analytic programming and four strategies of Differential evolution (canonical DE/Rand/1/Bin, chaos-based DE/Rand/1/Bin-Lozi, DE/Rand... 详细信息

ISBN: (纸本)9781538669792

关键词： analytic programming Differential Evolution Trigonometric identities

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DIFFERENT APPROACHES FOR CONSTANT ESTIMATION IN analytic programming 31

DIFFERENT APPROACHES FOR CONSTANT ESTIMATION IN ANALYTIC PRO...

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31st European Conference on Modelling and Simulation, ECMS 2017

作者： Oplatkova, Zuzana Kominkova Viktorin, Adam Senkerik, Roman Urbanek, Tomas Tomas Bata Univ Zlin Fac Appl Informat Nam TG Masaryka 5555 Zlin 76001 Czech Republic

ISBN: (纸本)9780993244049

This research deals with different approaches for constant estimation in analytic programming (AP). AP is a tool for symbolic regression tasks which enables to synthesise an analytical solution based on the required behaviour of the system. Some tasks do not need any constant estimation - AP is used in its basic version without any constant estimation handling. Compared to this, cases like data approximation need constants (coefficients) which are essential for the process of precise solution synthesis. This paper offers another strategy to already known and used by the AP from the very beginning and approaches published recently in 2016. This paper compares these procedures and the discussion also includes nonlinear fitting and metaevolutionary approach. As the main evolutionary algorithm, a differential algorithm (de/rand/1/bin) for the main process of AP is used.

关键词： analytic programming Differential evolution approximation sextic quintic

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Method of binary analytic programming to look for optimal mathematical 12th

Method of binary analytic programming to look for optimal ma...

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12th International Symposium on Intelligent Systems (INTELS)

作者： Diveev, A. I. Konyrbaev, N. B. Sofronova, E. A. Russian Acad Sci Fed Res Ctr Comp Sci & Control 44 Vavilova Str Moscow 119333 Russia RUDN Univ 6 Miklukho Maklaya Str Moscow 117198 Russia

In the known methods of symbolical regression by search of the solution with the help of a genetic algorithm, there is a problem of crossover. Genetic programming performs a crossover only in certain points. Grammatical evolution often corrects a code after a crossover. Other methods of symbolical regression use excess elements in a code for elimination of this shortcoming. The work presents a new method of symbolic regression on base of binary computing trees. The method has no problems with a crossover. Method use a coding in the form of a set of integer numbers like analytic programming The work describes the new method and some examples of codding for mathematical expressions. (C) 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license

关键词： symbolic regression genetic programming analytic programming genetic algorithm

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Performance Comparison of Differential Evolution Driving analytic programming for Regression

Performance Comparison of Differential Evolution Driving Ana...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Senkerik, Roman Voktorin, Adam Pluhacek, Michal Kadavy, Tomas Oplatkova, Zuzana Kominkova Tomas Bata Univ Zlin Fac Appl Informat Nam TG Masaryka 5555 Zlin 76001 Czech Republic

ISBN: (纸本)9781538627266

This research deals with the hybridization of symbolic regression open framework, which is analytical programming (AP) and Differential Evolution (DE) algorithm in the task of time series regression. This paper provides a closer insight into performance comparisons of connection between AP and different strategies of DE. AP can be considered as a powerful open framework for symbolic regression thanks to its applicability in any programming language with arbitrary driving evolutionary/swarm based algorithm. Thus, the motivation behind this research is to explore and investigate the differences in performance of AP driven by basic canonical strategies of DE as well as by the state of the art strategies, which is Success-History based Adaptive Differential Evolution (SHADE) and its variant L SHADE. Simple experiments have been carried out here with the four different time series of EUR/USD exchange rate. DE performance analysis, as well as the differences between regression models synthesized using AP as direct consequences of different DE strategies performances, are both discussed within conclusion section.

关键词： analytic programming Differential Evolution SHADE Time series regression

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Differential Evolution Driven analytic programming for Prediction 1

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16th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Senkerik, Roman Viktorin, Adam Pluhacek, Michal Kadavy, Tomas Zelinka, Ivan Tomas Bata Univ Fac Appl Informat Nam TG Masaryka 5555 Zlin 76001 Czech Republic Tech Univ Ostrava Fac Elect Engn & Comp Sci 17 Listopadu 15 Ostrava 70833 Czech Republic

ISBN: (数字)9783319590608

ISBN: (纸本)9783319590608;9783319590592

This research deals with the hybridization of symbolic regression open framework, which is analytical programming (AP) and Differential Evolution (DE) algorithm in the task of time series prediction. This paper provides a closer insight into applicability and performance of connection between AP and different strategies of DE. AP can be considered as powerful open framework for symbolic regression thanks to its applicability in any programming language with arbitrary driving evolutionary/swarm based algorithm. Thus, the motivation behind this research, is to explore and investigate the differences in performance of AP driven by basic canonical strategies of DE as well as by the state of the art strategy, which is Success-History based Adaptive Differential Evolution (SHADE). Simple experiment has been carried out here with the time series consisting of 300 data-points of GBP/USD exchange rate, where the first 2/3 of data were used for regression process and the last 1/3 of the data were used as a verification for prediction process. The differences between regression/prediction models synthesized by means of AP as a direct consequences of different DE strategies performances are briefly discussed within conclusion section of this paper.

关键词： analytic programming Differential evolution SHADE Time series prediction

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Hybridization of analytic programming and Differential Evolution for Time Series Prediction 12th

Hybridization of Analytic Programming and Differential Evolu...

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12th International Conference on Hybrid Artificial Intelligent Systems (HAIS)

作者： Senkerik, Roman Viktorin, Adam Pluhacek, Michal Kadavy, Tomas Zelinka, Ivan Tomas Bata Univ Zlin Fac Appl Informat Nam TG Masaryka 5555 Zlin 76001 Czech Republic Tech Univ Ostrava Fac Elect Engn & Comp Sci 17 Listopadu 15 Ostrava 70833 Czech Republic

ISBN: (纸本)9783319596501;9783319596495

This research deals with the hybridization of symbolic regression open framework, which is analytical programming (AP) and Differential Evolution (DE) algorithm in the task of time series prediction. This paper provides a closer insight into applicability and performance of the hybrid connection between AP and different strategies of DE. AP can be considered as a powerful open framework for symbolic regression thanks to its usability in any programming language with arbitrary driving evolutionary/swarm based algorithm. Thus, the motivation behind this research, is to explore and investigate the applicability and differences in performance of AP driven by basic canonical strategy of DE as well as by the state of the art strategy, which is Success-History based Adaptive Differential Evolution (SHADE). An experiment with three case studies has been carried out here with the several time series consisting of GBP/USD exchange rate, where the first 2/3 of data were used for regression process and the last 1/3 of the data were used as a verification for prediction process. The differences between regression/prediction models synthesized by means of AP as a direct consequences of different DE strategies performances are briefly discussed within conclusion section of this paper.

关键词： analytic programming Differential evolution SHADE Time series prediction

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