Heuristic Driven Genetic Algorithm for Priority Assignment of Real-Time Communications in NoC

2019 ◽  
pp. 433-445
Author(s):  
Ajay Khare ◽  
Chinmay Patil ◽  
Manikanta Nallamalli ◽  
Santanu Chattopadhyay
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Priority Assignment

Optimizing security and quality of service in a Real-time database system using Multi-objective genetic algorithm

2016 ◽  
Vol 64 ◽  
pp. 11-23 ◽  
Author(s):  
Xuancai Zhao ◽  
Qiuzhen Lin ◽  
Jianyong Chen ◽  
Xiaomin Wang ◽  
Jianping Yu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Quality Of Service ◽  
Real Time ◽  
Database System ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Real Time Database

scholarly journals Assigning Priorities for Fixed Priority Preemption Threshold Scheduling

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Saehwa Kim
Keyword(s):  
Real Time ◽  
Empirical Evaluation ◽  
Priority Assignment ◽  
Fixed Priority ◽  
Assignment Algorithm ◽  
Optimal Priority Assignment ◽  
A Performance

Preemption threshold scheduling (PTS) enhances real-time schedulability by controlling preemptiveness of tasks. This benefit of PTS highly depends on a proper algorithm that assigns each task feasible scheduling attributes, which are priority and preemption threshold. Due to the existence of an efficient optimal preemption threshold assignment algorithm that works with fully assigned priority orderings, we need an optimal priority assignment algorithm for PTS. This paper analyzes the inefficiency or nonoptimality of the previously proposed optimal priority assignment algorithms for PTS. We develop theorems for exhaustively but safely pruning infeasible priority orderings while assigning priorities to tasks for PTS. Based on the developed theorems, we correct the previously proposed optimal priority assignment algorithm for PTS. We also propose a performance improved optimal priority assignment algorithm for PTS proving its optimality. The empirical evaluation results clearly show the effectiveness of the proposed algorithm.

A real time computer aided object detection of nasopharyngeal carcinoma using genetic algorithm and artificial neural network based on Haar feature fear

2018 ◽  
Vol 89 ◽  
pp. 539-547 ◽  
Author(s):  
Mazin Abed Mohammed ◽  
Mohd Khanapi Abd Ghani ◽  
N. Arunkumar ◽  
Raed Ibraheem Hamed ◽  
Mohamad Khir Abdullah ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Nasopharyngeal Carcinoma ◽  
Object Detection ◽  
Real Time ◽  
Computer Aided ◽  
Haar Feature ◽  
Artificial Neural
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