GATutor: A Guided Discovery Based Tutor for Designing Greedy Algorithm

Alse, Kavya; Lahoti, Mukund; Verma, Meenakshi; Iyer, Sridhar

doi:10.1109/t4e.2015.26

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…Every search is the best choice for a strategy in the current state. Although there are not many scholars in the field of 3-SAT search for recent years, they have gained rapid development and application in some other fields [27][28][29][30] , so greedy algorithm has certain application value and research significance. In incomplete algorithms for solving 3-SAT problems, genetic algorithm is the main algorithm with global search ability.…”

Section: Introductionmentioning

confidence: 99%

An Improved Adaptive Genetic Algorithm for Solving 3-SAT Problems Based on Effective Restart and Greedy Strategy

Fu¹,

Xu²,

Wu³

et al. 2018

IJCIS

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An improved adaptive genetic algorithm is proposed for solving 3-SAT problems based on effective restart and greedy strategy in this paper. Several new characteristics of the algorithm are developed. According to the shortcomings of the adaptive genetic algorithm, it is easy to fall into the premature convergence and destroy optimal individual and make genetic performance decline. An improved adaptive genetic algorithm is proposed to adjust the crossover operator and mutation operator in different stages of evolution dynamically, and make use of the restart strategy to overcome the prematureness. At the same time, the algorithm adopts greedy strategy to make the maximum fitness value of each generation unabated, so as to accelerate the search for the optimal speed. In the experiment, several benchmark SAT problems in SATLIB are used to test the performance of the improved algorithm and the results are compared with those of other similar algorithms. The results show that the improved adaptive algorithm has a higher success ratio and faster solution speed.

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Section: Introductionmentioning

confidence: 99%

An Improved Adaptive Genetic Algorithm for Solving 3-SAT Problems Based on Effective Restart and Greedy Strategy

Fu¹,

Xu²,

Wu³

et al. 2018

IJCIS

View full text Add to dashboard Cite

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A hybrid virtual network mapping algorithm based on threshold load

Liu

Qin

Liu

et al. 2018

Satell Commun Network

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The virtual resource management architecture for satellite networks currently suffers from a very poor virtual network mapping success rate. This arises because of the need to map multiple heterogeneous virtual networks to the underlying satellite network. Most heuristic algorithms divide virtual network mapping into node mapping and link mapping, which aims to reduce the complexity of the problem.However, this approach is not well suited to highly dynamic satellite networks. In this paper, we propose a hybrid virtual network mapping algorithm that is based on threshold load. This takes the overall load for the nodes as its optimization objective, and combines the idea of backtracking contained in 1-stage mapping methods and the idea of global optimization contained in 2-stage mapping methods. The algorithm reduces the complexity of backtracking computation, while avoiding any incompleteness that might result from separating nodes and link mapping. The success rate for virtual network mapping is thus improved, as is the utilization rate for satellite network resources. KEYWORDSintegrated load, satellite networks, virtual network division, virtual network mapping

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