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The particle swarm algorithm is discussed in terms of social and cognitive behavior, though it is widely used as a problem solving method in engineering and computer science.
The concrete design process of test paper generating based on genetic algorithm is discussed in this paper, and some corresponding parameters setting have been compared and defined.
Even though the design algorithm is discussed in detail only for the ¯¯¯X&S chart, the general idea can be extended to many other charts such as the joint CUSUM charts and joint EWMA charts for detecting mean and variance shifts.
The complexity analysis of the proposed algorithm is discussed in Section 3.2.
The third curve of the enhanced fast algorithm is discussed in Section 5.1.
In the following section, the PNAE algorithm is discussed in detail.
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Experimental results of the algorithm are discussed in Section 3, followed by the conclusions and discussions of future work in Section 4.
The related theories, the structure design of ENN and the recognition algorithm are discussed in detail.
Advantages of a fuzzy predictive control algorithm are discussed in the paper.
Moreover, the optimizing performances of simple genetic algorithm and adaptive genetic algorithm are discussed in this paper.
The crossover and mutation operators for the proposed algorithm are discussed in what follows.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com