An Enhanced Genetic Programming Algorithm for Optimal Controller Design

Maher, Rami A.; Mohamed, Mohamed J.

doi:10.4236/ica.2013.41013

Cited by 13 publications

(11 citation statements)

References 8 publications

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“…(6) The closed system of the differential equations )) ( ( x h x, f x is locally unstable concerning x in unstable area n R X and is stable concerning the terminal point n R x * . For the solution of the task, we use a numerical method of symbolic regression which by means of an evolutionary algorithm will find the function meeting the specified requirements.…”

Section: Problem Statementmentioning

confidence: 99%

“…Application of numerical methods of symbolic regression for the solution of the problem of synthesis is already known [3][4][5][6][7][8][9]. Unlike the known numerical solutions of the synthesis problem, in the present work we use a new approach of synthesis of stable-unstable control of a plant.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the known methods of symbolic regression have an essential shortcoming for an algorithm of evolutionary search. In these methods [1][2][3][4][5][6][7][8][9] at search of the optimal solution by a genetic algorithm, it is necessary to carry out the main crossover operation in certain points, or it is essential to adjust the new solution after accomplishment of the crossover. Use of the principle of small variations [10] of the basic solution, as in variational methods of genetic and analytical programming [11][12][13], does not significantly change the situation.…”

Section: Introductionmentioning

confidence: 99%

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Complete Binary Variational Analytic Programming for Synthesis of Control at Dynamic Constraints

Diveev

Shmalko

2017

ITM Web Conf.

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Abstract. Dynamic constraints arise at control of the robot moving in the neighborhood of other robots. In this case, together with ensuring stability of the robot in a goal point it is necessary to provide instability for the robot in the area of state space where is some other robot. For the solution of this task, we use a new method of symbolic regression -a method of binary variational analytical programming. The new method has advantages in comparison with genetic or analytical programming in case of performing crossover, and it uses the principle of small variations of the basic solution to increase the efficiency of an algorithm of search of the optimal solution.

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Section: Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Complete Binary Variational Analytic Programming for Synthesis of Control at Dynamic Constraints

Diveev

Shmalko

2017

ITM Web Conf.

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“…Among the methods used for parameter tuning, computational intelligence based methods (population-based algorithms in particular) take an important place in the literature [9,14,42,47,51]. This group of methods also includes genetic programming [15,16,25,35,46]. The GP is based on processing of the population of individuals (by using evolutionary operators) and its evaluation.…”

Section: Introductionmentioning

confidence: 99%

Genetic Programming Algorithm for Designing of Control Systems

Cpałka¹,

Łapa²,

Przybył³

2018

ITC

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,andrzej.przybyl}@iisi.pcz.pl Genetic programming algorithms and other population-based methods are a convenient tool for solving complex interdisciplinary problems. Their characteristic feature is that they flexibly adapt to the problem and expectations of a designer. In this paper, they are used for designing complex control systems. In particular, a new approach for automatic designing of PID-based controllers which are resistant to noises in the measuring path is proposed. It is based on the knowledge about an object model and capabilities of the genetic algorithm and genetic programming. Not only do these make it possible to tune parameters and select the structure of PID-based controllers, but they also tune parameters of some additional components of a complex controller structure, like parameters of finite impulse response (FIR) filters. The idea of the proposed approach relies on proper encoding of the controller and a dedicated way of evolutionary processing of encoded solutions. The approach proposed in this paper has been tested using a typical control problem, i.e. a DC motor control problem.

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“…Due to the flexibility of this method, it is possible to make some changes to the classical algorithm in order to solve particular problems [33–35]. In the case of iron absorption, iron fluxes are determined by several factors, for instance, the intrinsic variability of the intestinal cells [36].…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling of Intestinal Iron Absorption Using Genetic Programming

et al. 2017

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Iron is a trace metal, key for the development of living organisms. Its absorption process is complex and highly regulated at the transcriptional, translational and systemic levels. Recently, the internalization of the DMT1 transporter has been proposed as an additional regulatory mechanism at the intestinal level, associated to the mucosal block phenomenon. The short-term effect of iron exposure in apical uptake and initial absorption rates was studied in Caco-2 cells at different apical iron concentrations, using both an experimental approach and a mathematical modeling framework. This is the first report of short-term studies for this system. A non-linear behavior in the apical uptake dynamics was observed, which does not follow the classic saturation dynamics of traditional biochemical models. We propose a method for developing mathematical models for complex systems, based on a genetic programming algorithm. The algorithm is aimed at obtaining models with a high predictive capacity, and considers an additional parameter fitting stage and an additional Jackknife stage for estimating the generalization error. We developed a model for the iron uptake system with a higher predictive capacity than classic biochemical models. This was observed both with the apical uptake dataset used for generating the model and with an independent initial rates dataset used to test the predictive capacity of the model. The model obtained is a function of time and the initial apical iron concentration, with a linear component that captures the global tendency of the system, and a non-linear component that can be associated to the movement of DMT1 transporters. The model presented in this paper allows the detailed analysis, interpretation of experimental data, and identification of key relevant components for this complex biological process. This general method holds great potential for application to the elucidation of biological mechanisms and their key components in other complex systems.

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An Enhanced Genetic Programming Algorithm for Optimal Controller Design

Cited by 13 publications

References 8 publications

Complete Binary Variational Analytic Programming for Synthesis of Control at Dynamic Constraints

Complete Binary Variational Analytic Programming for Synthesis of Control at Dynamic Constraints

Genetic Programming Algorithm for Designing of Control Systems

Mathematical Modeling of Intestinal Iron Absorption Using Genetic Programming

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