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Yen et al. [32] proposed a multi-constrained quality of service (QoS) multicast routing method using genetic algorithm.
Yen Yun-Sheng et al. [23] have proposed a multi-constrained QoS multicast routing method using genetic algorithm.
Endeavoring to solve the mathematical model more efficiently, we propose an effective heuristic method using Genetic Algorithm (GA).
The design and implementation of an on-line fuzzy identification method using genetic algorithms (GAs) is reported in this paper.
Multi-objective optimization method using genetic algorithm is employed in order to optimize design and operating parameters of a high temperature proton exchange membrane (HT-PEM) fuel cell based combined heat and power system.
A module based optimization method using genetic algorithms (GA), and multivariate regression analysis has been developed to optimize a set of parameters in the design of a nuclear reactor.
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The method uses genetic algorithm to derive the optimal weights.
The GA method uses genetic operands (crossover and mutation) to determine the best ECVs from a set of initial and new crossover ECVs.
This method uses genetic variations at multiple chromosomal locations and allows generation of sequence data, which are deposited in internet databases for comparison with DNA sequences of other isolates.
That focused the search still further and the team was then able to employ more modern methods using genetic markers to show which small fraction of chromosome 16 the apomixis genes inhabit.The three researchers are now beginning to analyse the DNA of that region in order to discover the exact make-up of its genes.
Recently, a few methods using genetic algorithm, particle swarm optimization and similar approaches have been proposed to handle this problem.
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