Application of adaptive genetic algorithm in mining industry

Authors:
G. Besiashvili;O. Rcheulishvili
Affiliations:
Department of Computer Sciences, Tbilisi State University, Tbilisi, Georgia;Georgian Technical University, Tbilisi, Georgia
Venue:
ECC'09 Proceedings of the 3rd international conference on European computing conference
Year:
2009

Citing 3
Cited 0

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Adaptive genetic algorithm with mutation and crossover matrices

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence

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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.