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首页> 外文期刊>International Journal on Data Science and Technology >Intelligence Classification of the Timetable Problem: A Memetic Approach
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Intelligence Classification of the Timetable Problem: A Memetic Approach

机译:时间表问题的智能分类:一种模因方法

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Scheduling tasks persist in our daily functioning and as academia, abounds more in University circle as hard-NP constraint satisfaction tasks. Many studies exist with the objective of resolving the many conflicted constraints that exists in a timetable schedule using various algorithms. Many of such algorithms simply search the domain space for a goal state that satisfies the problem constraints. Our study yields an outcome assignment that provides a complete, feasible and optimal academic schedule that satisfies medium cum hard constraints for the Federal University of Petroleum Resource Effurun in Delta State of Nigeria using memetic algorithm. Results showed that model converges after 4minutes and 29seconds; while its convergence time depends on use of belief space to ensure agents do not violate model bounds, encoding scheme used amongst others. The schedule considers both instructors and students' preference as medium constraints of high priority.
机译:日程安排任务在我们的日常工作中持续存在,并且作为学术界,在大学圈子中,更多的任务是硬性NP约束满足任务。存在许多研究的目的是使用各种算法来解决时间表中存在的许多冲突约束。许多这样的算法只是在域空间中搜索满足问题约束的目标状态。我们的研究产生了一个成果分配,该成果提供了完整,可行和最佳的教学计划,该计划使用模因算法满足尼日利亚三角州的联邦石油大学埃弗弗伦分校的中等难度和硬性约束。结果表明,模型在4分钟和29秒后会收敛;虽然其收敛时间取决于对信念空间的使用,以确保代理不违反模型边界,但编码方案尤其如此。该时间表将教师和学生的偏爱都视为高优先级的中等限制条件。

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