Static Task Scheduling Using Genetic Algorithm and Reinforcement Learning

2007 ◽  
Author(s):  
Mohammad Moghimi Najafabadi ◽  
Mustafa Zali ◽  
Shamim Taheri ◽  
Fattaneh Taghiyareh
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Task Scheduling

scholarly journals A Kind of Reinforcement Learning to Improve Genetic Algorithm for Multiagent Task Scheduling

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhipeng Li ◽  
Xiumei Wei ◽  
Xuesong Jiang ◽  
Yewen Pang
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Task Scheduling ◽  
Intelligent Control ◽  
Manufacturing Systems ◽  
Task Assignment ◽  
Control Agent ◽  
Multiple Production ◽  
Intelligent Control System ◽  
Standard Task

It is difficult to coordinate the various processes in the process industry. We built a multiagent distributed hierarchical intelligent control model for manufacturing systems integrating multiple production units based on multiagent system technology. The model organically combines multiple intelligent agent modules and physical entities to form an intelligent control system with certain functions. The model consists of system management agent, workshop control agent, and equipment agent. For the task assignment problem with this model, we combine reinforcement learning to improve the genetic algorithm for multiagent task scheduling and use the standard task scheduling dataset in OR-Library for simulation experiment analysis. Experimental results show that the algorithm is superior.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

scholarly journals Computational Design of Modular Robots Based on Genetic Algorithm and Reinforcement Learning

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 471
Author(s):  
Jai Hoon Park ◽  
Kang Hoon Lee
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Design Space ◽  
Learning Algorithm ◽  
Computational Design ◽  
Computational Method ◽  
Learning Ability ◽  
Modular Robots ◽  
Control Mechanisms ◽  
Candidate Structure

Designing novel robots that can cope with a specific task is a challenging problem because of the enormous design space that involves both morphological structures and control mechanisms. To this end, we present a computational method for automating the design of modular robots. Our method employs a genetic algorithm to evolve robotic structures as an outer optimization, and it applies a reinforcement learning algorithm to each candidate structure to train its behavior and evaluate its potential learning ability as an inner optimization. The size of the design space is reduced significantly by evolving only the robotic structure and by performing behavioral optimization using a separate training algorithm compared to that when both the structure and behavior are evolved simultaneously. Mutual dependence between evolution and learning is achieved by regarding the mean cumulative rewards of a candidate structure in the reinforcement learning as its fitness in the genetic algorithm. Therefore, our method searches for prospective robotic structures that can potentially lead to near-optimal behaviors if trained sufficiently. We demonstrate the usefulness of our method through several effective design results that were automatically generated in the process of experimenting with actual modular robotics kit.

Reinforcement Learning in Dynamic Task Scheduling: A Review

2020 ◽  
Vol 1 (6) ◽  
Author(s):  
Chathurangi Shyalika ◽  
Thushari Silva ◽  
Asoka Karunananda
Keyword(s):  
Reinforcement Learning ◽  
Task Scheduling ◽  
Dynamic Task ◽  
Dynamic Task Scheduling

Multiphase fault tolerance genetic algorithm for vm and task scheduling in datacenter

2021 ◽  
Vol 58 (5) ◽  
pp. 102676
Author(s):  
Samira Kanwal ◽  
Zeshan Iqbal ◽  
Fadi Al-Turjman ◽  
Aun Irtaza ◽  
Muhammad Attique Khan
Keyword(s):  
Genetic Algorithm ◽  
Fault Tolerance ◽  
Task Scheduling ◽  
And Task
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