GA-based principal component selection for production performance estimation in mineral processing

In this paper, a genetic algorithm (GA) based principal component selection approach is proposed for production performance estimation in mineral processing. The approach combines a modified GA with principal component analysis (PCA) in order to improve the estimation accuracy of production performance. In this context, the extended chromosome encoding, the fitness function formed by combining the prediction performance operator and the penalty function is designed based on the standard GA. Both the mutation allele number operator and the allele mutation possibility operator are also introduced in the mutation process of chromosome. The proposed approach can select the principal components which are crucial for estimation performance, and the useful message from PCA can guide the evolution of GA and accelerate the convergence process. The case studies have been carried out on the prediction of the production rate and concentrate grade of a mineral process and the experimental results show the effectiveness of the proposed approach.