Customizable FPGA IP Core Implementation of a General-Purpose Genetic Algorithm Engine

Fernando, Pradeep; Katkoori, Srinivas; Keymeulen, Didier; Zebulum, R.; Stoica, Adrian

doi:10.1109/tevc.2009.2025032

Cited by 71 publications

(40 citation statements)

References 21 publications

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“…To show the influence of n and the validity of the proposed algorithm, we compare a survival-based GA with a fixed mutation probability and its performance is investigated by solving complex problems with local minima such as DeJong function, Rastrigin function, and mathematical function [1]. For the experiment, the used population size, probability of 1-point crossover, length L and maximum generation number T are 128, 1, 30 bits and 10000 respectively.…”

Section: Performance Evaluation and Numerical Resultsmentioning

confidence: 99%

“…The use of the genetic algorithm (GA) has been steadily increased as main adaptive mechanism to solve many large-scaled optimization problems because of its robust capability of exploring solution spaces of a given problem [1]. The general genetic algorithm uses several simple operations in order to simulate evolution.…”

Section: Introductionmentioning

confidence: 99%

“…However, GA takes a long computation time due to its iterative operations and has a local minima problem caused by adaptive evolution mechanism. Therefore, efficient hardware implementation for realtime applications and mutation rate adjustment scheme for preventing from local minima are very important [1,4]. In this reason, we propose an efficient self-adjusting method for GAP, which selects optimal mutation probability according to the change of population diversities.…”

Section: Introductionmentioning

confidence: 99%

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Improved mutation method for providing high genetic diversity of genetic algorithm processor

Kim

Lee

2012

IEICE Electron. Express

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Abstract:Hardware implementation of genetic algorithm processor (GAP) is important for proven effectiveness as optimization engines for real-time solutions. To implement the robust GAP, it is significant to maintain the population diversity for sustaining the convergence capacity and preventing local optimum problem. In this reason, we propose a deterministic mutation method for providing the high population diversity to GAP. Experimental results with mathematical problems and pattern recognition show that the proposed method enhances the convergence capacity up to 34.5% and reduces computation power about 40% compared with the conventional method.

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Section: Performance Evaluation and Numerical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved mutation method for providing high genetic diversity of genetic algorithm processor

Kim

Lee

2012

IEICE Electron. Express

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“…Paper [95] presents how a self-generated architecture may be used to build-up a secret physical cipher unit with dynamic security properties. Another application can be found in [96], where a run-time adaptable evolvable hardware classifier system is proposed or in [97], in which a FPGA based customizable generalpurpose GA engine has been reported. A novel bio-inspired self-test technique for the implementation of evolvable fault tolerant systems, which mimics a Unitronic (unicellular electronic) artificial system, is presented in [98].…”

Section: Evolvable Hardwarementioning

confidence: 99%

FPGAs in Industrial Control Applications

Monmasson

Idkhajine

Cirstea

et al. 2011

IEEE Trans. Ind. Inf.

459

182

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-The aim of this paper is to review the state of the art of Field Programmable Gate Array (FPGA) technologies and their contribution in industrial control applications. Authors start by addressing various research fields where FPGAs are useful. The features of these devices are then presented followed by their corresponding design tools. To illustrate the benefit of using FPGAs in the case of complex control applications, a sensorless motor controller has been treated. This controller is based on the Extended Kalman Filter. Its development has been made according to a dedicated design methodology which is also discussed. Another example of illustration is the Neural Network Systems. To show the interest of FPGAs in this field, some case studies have been presented.

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“…GA is applied in a variety of significant problems, such as customizable FPGA IP core, the traveling salesman problem, network routing problems, stereo matching and clustering data [12][13][14][15]. The GA has four steps as follows:…”

Section: Genetic Algorithmmentioning

confidence: 99%

An Improved Signal Segmentation Method using Genetic Algorithm

Azami¹,

Mohammadi²,

Hassanpour³

2011

IJCA

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In many signal processing application, the signal of interest is often divided into epochs. In these applications, the segmented signal is preferred to have no change on the statistical characteristics of the epochs. Modified Varri is among the segmentation methods with an acceptable accuracy. There are three parameters affecting on the accuracy of this method. These parameters are set experimentally. Hence, they may not be optimal for any signal segmentation application. We have used Genetic Algorithm (GA) in this research to choose appropriate values for these parameters in any signal segmentation application. The proposed technique was applied on both synthetic signal and Electroencephalography (EEG) to evaluate its performance. The results indicate superiority of the proposed method in signal segmentation compared to the original approach.

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Customizable FPGA IP Core Implementation of a General-Purpose Genetic Algorithm Engine

Cited by 71 publications

References 21 publications

Improved mutation method for providing high genetic diversity of genetic algorithm processor

Improved mutation method for providing high genetic diversity of genetic algorithm processor

FPGAs in Industrial Control Applications

An Improved Signal Segmentation Method using Genetic Algorithm

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