Arranging examination timetable is problematic. It differs from other timetabling problems in terms of conditions. A complete timetable must reach several requirements involving course, group of student sitting the ex...
详细信息
ISBN:
(纸本)9781467355803;9781467355780
Arranging examination timetable is problematic. It differs from other timetabling problems in terms of conditions. A complete timetable must reach several requirements involving course, group of student sitting the exam in that course, etc. It is similar to the course's timetable but not the same. Many differences between them include the way to create and the requirements. This paper proposes an adaptive genetic algorithm model applied for improving effectiveness of automatic arranging examination timetable. Hard constraints and soft constraints for this specific problem were discussed. In addition, the genetic elements were designed and the penalty cost function was proposed. Three genetic operators: crossover, mutation, and selection were employed. A simulation was conducted to obtain some results. The results show that the proposed GA model works well in arranging an examination timetable. With 0.75 crossover rate, there is no hard constraints appeared in the timetable.
Arranging examination timetable is problematic. It differs from other timetabling problems in terms of conditions. A complete timetable must reach several requirements involving course, group of student sitting the ex...
详细信息
ISBN:
(纸本)9781467355797
Arranging examination timetable is problematic. It differs from other timetabling problems in terms of conditions. A complete timetable must reach several requirements involving course, group of student sitting the exam in that course, etc. It is similar to the course’s timetable but not the same. Many differences between them include the way to create and the requirements. This paper proposes an adaptive genetic algorithm model applied for improving effectiveness of automatic arranging examination timetable. Hard constraints and soft constraints for this specific problem were discussed. In addition, the genetic elements were designed and the penalty cost function was proposed. Three genetic operators: crossover, mutation, and selection were employed. A simulation was conducted to obtain some results. The results show that the proposed GA model works well in arranging an examination timetable. With 0.75 crossover rate, there is no hard constraints appeared in the timetable.
作者：
Yuan-long LiJun ZhangSun Yat Sen Univ
Dept Comp Sci Key Lab Digital Life Minist EducKey Lab Software TechnolEduc Dept Gu Guangzhou Peoples R China
This paper will introduce a new differential evolution (DE) algorithm called DE/cluster. DE/cluster applies a simple hierarchical clustering model to mine the distribution information of the DE population every K gene...
详细信息
ISBN:
(纸本)9781450305570
This paper will introduce a new differential evolution (DE) algorithm called DE/cluster. DE/cluster applies a simple hierarchical clustering model to mine the distribution information of the DE population every K generations to make a dynamic partition of the population. One special cluster formed by the single-individual clusters will use a slower convergence mutation strategy to do the global search. The other clusters will use more greedy searching strategy to do the local search. As long as the subpopulations may be trapped by local minima, the "dead" state is defined for a cluster and clusters will be checked in every generation and the "dead" clusters will be restarted in the current searching range. This local restart strategy can make the performance of DE/cluster even be better than DE/rand on some multimodal test functions that are not linearly separable. The DE/cluster algorithm is tested on a test suite with 24 functions and it shows promising performance compared with the current best DE variants.
Ubiquitous researches had been done recently. With the development of cellular phones and internet, these individualised programs exist in the digital world where a new ecosystem can be formed. Competition and collabo...
详细信息
Ubiquitous researches had been done recently. With the development of cellular phones and internet, these individualised programs exist in the digital world where a new ecosystem can be formed. Competition and collaboration thus exists among different groups of autonomous entities. We propose a model like food web exists in natural world and serves as a balancing algorithm among different types of species. We define the food web model and propose algorithms for formation and communication. The proposed model can be used to consider the development of intelligent strategies and the underlying communication topology such that ubiquitous intelligences can be evolved on the digital food web.
Ubiquitous power includes one important factor that is the coordination among small instances of individualized smart programs. With the coordination, intelligent strategies can be established to realize computing top...
详细信息
ISBN:
(纸本)3540380914
Ubiquitous power includes one important factor that is the coordination among small instances of individualized smart programs. With the coordination, intelligent strategies can be established to realize computing topology and to serve different purposes of computing goals. With the development of cellular phones and internet, these individualized programs exist in the digital world where a new ecosystem can be formed. These smart entities create groups and consume natural resources in the digital world. Competition and collaboration thus exists among different groups of autonomous entities. We propose a model, based on a concept called food web in ecosystem. Food web exists in natural words for minions of years and serves as a balancing algorithm among different types of species. We define the food web model for autonomous entities and propose algorithms for formation and communication. As a consequence, the proposed model can be used to consider the development of intelligent strategies and the underlying communication topology such that ubiquitous intelligences can be evolved on the digital food web.
暂无评论