An ideally design of blast pattern parameters can cause improving blasting results by reducing undesirable phenomena such as flyrock and backbreak. In this paper, a new combination of artificial neural network (ANN) and genetic algorithm (GA) was implemented to design optimized blast parameters of the Soungun copper mine. Firstly, ANN model was trained to predict the undesirable phenomena. And secondly, the trained model was considered as fitness function for GA to optimize blast parameters aiming to minimize the operation side effects. By this way, GA produced 32 different blast patterns which simultaneously satisfy both the flyrock and backbreak. Application of the patterns 1 and 32 would result in minimum flyrock and backbreak of 25.32m and 0.52m, respectively. However, the pattern 14 provides the best balancing condition with flyrock and backbreak of 27.96m and 0.71m, respectively.
