A Genetic Algorithm with Zooming for the Determination of the Optimal Open Pit Mines Layout

Abstract

A Genetic Algorithm (GA) with nested zooming strategy is proposed for the determination of the optimal open pit mine design.

Different genetic procedures are applied to increase robustness, namely two typologies of admissible mutations for the elite sub-population subjected to zooming and mutation and reproduction for the remaining individuals. In order to further improve convergence rate, a user-defined population percentage, depending on individuals fitness, is replaced with new phenotypes, enforcing chromosomic renewal.

Several comparisons with (traditionally used) dynamic programming approaches are provided both for 2D and 3D open pit mines. Both small and large scale mines are analyzed, to benchmark the code in presence of several variables.

Results show that the procedure proposed requires a very limited computational effort, both for challenging problems with several variables and when a micro-GA (populations with few individuals) is adopted for small scale problems.