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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.
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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.
Both binary amplitude and phase SOLs are designed with an efficient optimization method using genetic algorithm and fast Hankel transform algorithm either in oil immersion medium or in air.
In this research, a model-based optimal design method using genetic algorithm is developed to optimize the capacity of active CTES for maximizing the life-cycle cost saving including capital cost associated with storage capacity as well as incentives from both fast DR and PLM.
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