The Experts below are selected from a list of 554673 Experts worldwide ranked by ideXlab platform
Darrell Whitley - One of the best experts on this subject based on the ideXlab platform.
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A Genetic Algorithm tutorial
Statistics and Computing, 1994Co-Authors: Darrell WhitleyAbstract:This tutorial covers the canonical Genetic Algorithm as well as more experimental forms of Genetic Algorithms, including parallel island models and parallel cellular Genetic Algorithms. The tutorial also illustrates Genetic search by hyperplane sampling. The theoretical foun- dations of Genetic Algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical Genetic Algorithm.
Salim Chikhi - One of the best experts on this subject based on the ideXlab platform.
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Comparison of Genetic Algorithm and Quantum Genetic Algorithm
Ccis2K.Org, 2012Co-Authors: Zakaria Laboudi, Salim ChikhiAbstract:Evolving solutions rather than computing them certainly represents a promising programming approach. Evolutionary computation has already been known in computer science since more than 4 decades. More recently, another alternative of evolutionary Algorithms was invented: Quantum Genetic Algorithms (QGA). In this paper, we outline the approach of QGA by giving a comparison with Conventional Genetic Algorithm (CGA). Our results have shown that QGA can be a very promising tool for exploring search spaces.
M. Valverde - One of the best experts on this subject based on the ideXlab platform.
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ETFA - Inverter for microturbines based on multiobjective Genetic Algorithm
2005 IEEE Conference on Emerging Technologies and Factory Automation, 2005Co-Authors: Francisco Jurado, M. ValverdeAbstract:In this paper, authors present a new design method for pulse width modulation inverters in microturbines by using a multiobjective Genetic Algorithm. The design problem is converted to an equivalent optimization problem, and then a multiobjective Genetic Algorithm is adopted to find a solution. The Genetic Algorithm is proposed to design a fuzzy controller. In this GA approach, an individual is constructed to represent the fuzzy controller. Multiobjective Genetic Algorithm confers a number of advantages over conventional multiobjective optimization methods by evolving a family of Pareto-optimal solutions rather than a single solution estimate. This optimal fuzzy controller is suitable for the specific harmonic elimination PWM technology, as demonstrated by the examples given in this paper
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Multiobjective Genetic Algorithm for Three-Phase PWM Inverter in Microturbines
Electric Power Components and Systems, 2005Co-Authors: Francisco Jurado, M. ValverdeAbstract:In this article, authors present a new design method for PWM inverters in microturbines by using a multiobjective Genetic Algorithm. More precisely, the design problem is converted to an equivalent optimization problem, and then a multiobjective Genetic Algorithm is adopted to find a solution. The Genetic Algorithm is proposed to design a fuzzy controller. In this Genetic Algorithm approach, an individual is constructed to represent the fuzzy controller. A short coded string is proposed such that it is associated with an individual can map, a fuzzy controller structure, including the membership functions, and the scaling factors. Multiobjective Genetic Algorithm confers a number of advantages over conventional multiobjective optimization methods by evolving a family of Pareto-optimal solutions rather than a single solution estimate. This optimal fuzzy controller is suitable for the specific harmonic elimination pulse width modulation technology, as demonstrated by the examples given in this article.
Zakaria Laboudi - One of the best experts on this subject based on the ideXlab platform.
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Comparison of Genetic Algorithm and Quantum Genetic Algorithm
Ccis2K.Org, 2012Co-Authors: Zakaria Laboudi, Salim ChikhiAbstract:Evolving solutions rather than computing them certainly represents a promising programming approach. Evolutionary computation has already been known in computer science since more than 4 decades. More recently, another alternative of evolutionary Algorithms was invented: Quantum Genetic Algorithms (QGA). In this paper, we outline the approach of QGA by giving a comparison with Conventional Genetic Algorithm (CGA). Our results have shown that QGA can be a very promising tool for exploring search spaces.
Francisco Jurado - One of the best experts on this subject based on the ideXlab platform.
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ETFA - Inverter for microturbines based on multiobjective Genetic Algorithm
2005 IEEE Conference on Emerging Technologies and Factory Automation, 2005Co-Authors: Francisco Jurado, M. ValverdeAbstract:In this paper, authors present a new design method for pulse width modulation inverters in microturbines by using a multiobjective Genetic Algorithm. The design problem is converted to an equivalent optimization problem, and then a multiobjective Genetic Algorithm is adopted to find a solution. The Genetic Algorithm is proposed to design a fuzzy controller. In this GA approach, an individual is constructed to represent the fuzzy controller. Multiobjective Genetic Algorithm confers a number of advantages over conventional multiobjective optimization methods by evolving a family of Pareto-optimal solutions rather than a single solution estimate. This optimal fuzzy controller is suitable for the specific harmonic elimination PWM technology, as demonstrated by the examples given in this paper
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Multiobjective Genetic Algorithm for Three-Phase PWM Inverter in Microturbines
Electric Power Components and Systems, 2005Co-Authors: Francisco Jurado, M. ValverdeAbstract:In this article, authors present a new design method for PWM inverters in microturbines by using a multiobjective Genetic Algorithm. More precisely, the design problem is converted to an equivalent optimization problem, and then a multiobjective Genetic Algorithm is adopted to find a solution. The Genetic Algorithm is proposed to design a fuzzy controller. In this Genetic Algorithm approach, an individual is constructed to represent the fuzzy controller. A short coded string is proposed such that it is associated with an individual can map, a fuzzy controller structure, including the membership functions, and the scaling factors. Multiobjective Genetic Algorithm confers a number of advantages over conventional multiobjective optimization methods by evolving a family of Pareto-optimal solutions rather than a single solution estimate. This optimal fuzzy controller is suitable for the specific harmonic elimination pulse width modulation technology, as demonstrated by the examples given in this article.
