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A genetic algorithm applied in the optimization of cognitive profiles: A nonlinear dynamic system resolution

机译：一种应用在优化认知配置文件中的遗传算法：非线性动态系统分辨率

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Genetic Algorithms (GAs) are gradient-free optimization algorithms that use a performance criterion for evaluation and a population of possible solutions to the search for a global optimum [2]. They are capable of handling complex and irregular solution spaces, and have been applied to various difficult problems. Inspired by the biological process of Darwinism evolution where selection, mutation and crossover play a major role, GAs can handle high-dimensional nonlinear optimization problems [1]. Therefore, our proposed approach is based on the evolvement technique through a structure named knowledge blocks (KBs). Then, the GA and a pre-evaluation statistical function are employed to construct an optimal path for each learner [3]. This paper makes three critical contributions: (1) it presents a genetic-based curriculum sequencing approach that will generate a personalized curriculum sequencing supported by the KBs; (2) it creates the bases of a new paradigm - the Knowledge Block Structure (KBS) - as a standard to implement a new learning path; and (3) it uses empirical research to indicate that the proposed approach can conduct to a correct learning path, based on individual learner needs.
机译：遗传算法（气体）是无梯度优化算法，用于评估性能标准和搜索全球最优的可能解决方案的群体。它们能够处理复杂和不规则的解决方案空间，并已应用于各种难题。受到达尔文主义演化的生物过程的启发，其中选择，突变和交叉发挥着重要作用，气体可以处理高维非线性优化问题[1]。因此，我们所提出的方法是通过名为知识块（KBS）的结构的演变技术。然后，采用Ga和预评估统计功能来构建每个学习者的最佳路径[3]。本文提出了三种关键贡献：（1）它提出了一种基于遗传的课程测序方法，将产生由KBS支持的个性化课程测序; （2）它创建了一个新的范式的基础 - 知识块结构（KBS） - 作为实施新的学习路径的标准; （3）它使用实证研究表明，基于个别学习者需求，所提出的方法可以对正确的学习路径进行。

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