DocumentCode
1675501
Title
Application of Adaptive genetic algorithm in mining industry
Author
Besiashvili, G. ; Khachidze, M. ; Chokhonelidze, D.
Author_Institution
Tbilisi State Univ., Tbilisi, Georgia
fYear
2012
Firstpage
1
Lastpage
3
Abstract
Selection, mutation and crossover are the parameters that stipulate the evolution process. These three methods are used by the genetic algorithms. We´ve tried to apply genetic algorithms in mining industry, particularly in concentrating the manganese. It´s necessary to optimize several parameters for that. In Adaptive genetic algorithms were applied Hamming weight and Hamming distance for selection and crossover. By Hamming Distance we search in chromosomes the similarity combinations and define the crossover point.
Keywords
genetic algorithms; manganese; mining industry; Hamming distance; Hamming weight; adaptive genetic algorithm; crossover point; evolution process; manganese concentration; mining industry; mutation; parameter optimization; selection; similarity combination; Hamming distance; Hamming weight; fitness function; genetic algorithm;
fLanguage
English
Publisher
ieee
Conference_Titel
Problems of Cybernetics and Informatics (PCI), 2012 IV International Conference
Conference_Location
Baku
Print_ISBN
978-1-4673-4500-2
Type
conf
DOI
10.1109/ICPCI.2012.6486320
Filename
6486320
Link To Document