Genetic Algorithm using Theory of Chaos

Snaselova, Petra; Zboril, Frantisek

doi:10.1016/j.procs.2015.05.248

Cited by 48 publications

(27 citation statements)

References 4 publications

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“…In this section we discuss the relation between the equilibrium points of the delay system (8) and the fixed points of the discrete system (6). These solutions are the simplest dynamical behavior a dynamical system can display.…”

Section: Equilibrium Pointsmentioning

confidence: 99%

“…Also, we discuss some conditions for an equilibrium point be stable, which will be used in the next section. (8) is differentiable. Suppose also that, for a given , 0 is a fixed point of ( , ) and the derivative of this map is defined at ( 0 , ).…”

Section: Equilibrium Pointsmentioning

confidence: 99%

“…Control and synchronization of chaos have become an intense field of research since several years ago. Some publications pointed out the impact this field may have in many areas of science and technology [1][2][3][4][5][6][7][8]. Chaos anticontrol, that is, producing chaos in a controlled way in a nonchaotic system, has also attracted attention due to its potential usefulness for some important problems of mechanical, electronic, telecommunications, optical, chemical, or biological systems, among others [9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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Oscillations in First‐Order, Continuous‐Time Systems via Time‐Delay Feedback

Salcedo

Álvarez

2018

Complexity

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A technique to generate (periodic or nonperiodic) oscillations systematically in first-order, continuous-time systems via a nonlinear function of the state, delayed by a certain time d, is proposed. This technique consists in choosing a nonlinear function of the delayed state with some passivity properties, tuning a gain to ensure that all the equilibrium points of the closed-loop system be unstable, and then imposing conditions on the closed-loop system to be semipassive. We include several typical examples to illustrate the effectiveness of the proposed technique, with which we can generate a great variety of chaotic attractors. We also include a physical example built with a simple electronic circuit that, after applying the proposed technique, displays a similar behavior to the logistic map.

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Section: Equilibrium Pointsmentioning

confidence: 99%

Section: Equilibrium Pointsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Oscillations in First‐Order, Continuous‐Time Systems via Time‐Delay Feedback

Salcedo

Álvarez

2018

Complexity

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“…Chaos is a class of random phenomena which appear to be chaotic and exist fine rules in a definite system [14]. Because chaos has the ergodic property, it is better to use chaotic variables to optimize the search than blind random search, and prevent the evolutionary algorithm from being trapped into the local optimum.…”

Section: Improved Genetic Algorithmmentioning

confidence: 99%

Study on the Method of Face Detection Based on Chaos Genetic Algorithm Optimization AdaBoost Algorithm

Chai

Yang

2017

MATEC Web Conf.

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Abstract. Aiming at the problems that the traditional AdaBoost algorithm has complex feature computation, long training time and low detection rate, a method of face detection based on chaos genetic algorithm optimization adaBoost algorithm was proposed. Firstly, this algorithm uses the image color segmentation for coarse screening on the face image, in order to determine the human skin area. Secondly, the adaptive median filtering is applied to denoise the face image to improve the quality of the face image. Finally, the chaotic genetic algorithm is used to optimize the AdaBoost algorithm to achieve higher detection rate and detection speed. Compared with the traditional AdaBoost algorithm, the experimental results showed that the face detection method based on chaos genetic algorithm optimization AdaBoost algorithm proposed in this paper has a significant improvement in detection rate and detection speed.

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“…Every time is more clear that evolution in nature is chaotic, because the populations are dynamic in size; mutation characteristics, crossing, and selection are defined by the chaotic determinism of each species and its environment. This structural modification established the chaotic genetic algorithms (CGA), which have allowed to address new applications in the search of global optimum in the spaces of search using chaotic maps for the generation of the populations of individuals of the algorithm [17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

The Effect of Entropy on the Performance of Modified Genetic Algorithm Using Earthquake and Wind Time Series

et al. 2018

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The dynamic complexity of time series of natural phenomena allowed to improve the performance of the genetic algorithm to optimize the test mathematical functions. The initial populations of stochastic origin of the genetic algorithm were replaced using the series of time of winds and earthquakes. The determinism of the time series brings in more information in the search of the global optimum of the functions, achieving reductions of time and an improvement of the results. The information of the initial populations was measured using the entropy of Shannon and allowed to establish the importance of the entropy in the initial populations and its relation with getting better results. This research establishes a new methodology for using determinism time series to search the best performance of the models of optimization of genetic algorithms (GA).

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Genetic Algorithm using Theory of Chaos

Cited by 48 publications

References 4 publications

Oscillations in First‐Order, Continuous‐Time Systems via Time‐Delay Feedback

Oscillations in First‐Order, Continuous‐Time Systems via Time‐Delay Feedback

Study on the Method of Face Detection Based on Chaos Genetic Algorithm Optimization AdaBoost Algorithm

The Effect of Entropy on the Performance of Modified Genetic Algorithm Using Earthquake and Wind Time Series

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