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 :
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