A new process variable and dynamics selection method based on a genetic algorithm-based wavelength selection method

AIChE Journal ◽  
2012 ◽  
Vol 58 (6) ◽  
pp. 1829-1840 ◽  
Author(s):  
Hiromasa Kaneko ◽  
Kimito Funatsu
Keyword(s):  
Genetic Algorithm ◽  
Process Variable ◽  
Selection Method ◽  
Wavelength Selection ◽  
New Process

Genetic algorithm-based wavelength selection method for spectral calibration

2010 ◽  
Vol 25 (1) ◽  
pp. 10-19 ◽  
Author(s):  
Masamoto Arakawa ◽  
Yosuke Yamashita ◽  
Kimito Funatsu
Keyword(s):  
Genetic Algorithm ◽  
Selection Method ◽  
Wavelength Selection ◽  
Spectral Calibration

An Effective Genetic Algorithm-Based Feature Selection Method for Intrusion Detection Systems

2021 ◽  
pp. 102448
Author(s):  
Zahid Halim ◽  
Muhammad Nadeem Yousaf ◽  
Muhammad Waqas ◽  
Muhammad Suleman ◽  
Ghulam Abbas ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Intrusion Detection Systems ◽  
Detection Systems

scholarly journals Feature Selection Method using Genetic Algorithm for Medical Dataset

2019 ◽  
Vol 9 (6) ◽  
pp. 1907
Author(s):  
Neesha Jothi ◽  
Wahidah Husain ◽  
Nur’Aini Abdul Rashid ◽  
Sharifah Mashita Syed-Mohamad
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Medical Dataset

scholarly journals Efficient Scheduling of Plantation Company Workers using Genetic Algorithm

2020 ◽  
Vol 3 (2) ◽  
pp. 60
Author(s):  
Wayan Firdaus Mahmudy ◽  
Andreas Pardede ◽  
Agus Wahyu Widodo ◽  
Muh Arif Rahman
Keyword(s):  
Genetic Algorithm ◽  
Physical Health ◽  
Selection Method ◽  
Computational Experiments ◽  
Cut Point ◽  
Chromosome Representation ◽  
Plantation Company ◽  
Reciprocal Mutation ◽  
Best Parameters ◽  
Work Scheduling

Workers at large plantation companies have various activities. These activities include caring for plants, regularly applying fertilizers according to schedule, and crop harvesting activities. The density of worker activities must be balanced with efficient and fair work scheduling. A good schedule will minimize worker dissatisfaction while also maintaining their physical health. This study aims to optimize workers' schedules using a genetic algorithm. An efficient chromosome representation is designed to produce a good schedule in a reasonable amount of time. The mutation method is used in combination with reciprocal mutation and exchange mutation, while the type of crossover used is one cut point, and the selection method is elitism selection. A set of computational experiments is carried out to determine the best parameters’ value of the genetic algorithm. The final result is a better 30 days worker schedule compare to the previous schedule that was produced manually. 

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