A Simple Genetic Algorithm for Calibration of Stochastic Rock Discontinuity Networks

Abstract

We present a novel approach for calibration of stochastic discontinuity network parameters based on genetic algorithms (GAs). To validate the approach, examples of application of the method to cases with known parameters of the original Poisson discontinuity network are presented. Parameters of the model are encoded as chromosomes using a binary representation, and such chromosomes evolve as successive generations of a randomly generated initial population, subjected to GA operations of selection, crossover and mutation. Such backcalculated parameters are employed to make assessments about the inference capabilities of the model using different objective functions with different probabilities of crossover and mutation. Results show that the predictive capabilities of GAs significantly depend on the type of objective function considered; and they also show that the calibration capabilities of the genetic algorithm can be acceptable for practical engineering applications, since in most cases they can be expected to provide parameter estimates with relatively small errors for those parameters of the network (such as intensity and mean size of discontinuities) that have the strongest influence on many engineering applications.

Discharge evaluation from pressure measurements by a genetic algorithm based method

“…The idea of using the GA methods for numerical models calibration was already applied in various technical and nontechnical domains, because of their wide flexibility and ability to find some near optimal solutions without computational difficulties [14][15][16][17][18]. In a recent paper Neagoe and Popa [19] presented a GA calibration of the transient flow model for the water supply system in a hydropower plant.…”

A Novel Approach to Combinatorial Problems: Binary Growth Optimizer Algorithm

“…Optimization Algorithm Application Convergence Complexity [22] GA Parameter calibration Low Low [23] Binary PSO Input variable selection in ELM Medium High [24] Binary PSO Parameter optimization in ELM Medium High [25] GA Variable selection in hot metal desulfurization kinetics Low Low [26] Binary GWO ESN Low High [27] Binary CSO Parameter optimization in MRE isolator Low High [28] CSO and salp swarm algorithm CNN Medium High [29] Binary CSA CNN Low High [30] DE and binary DE NN Medium High Table 1. Cont.…”

A survey on binary metaheuristic algorithms and their engineering applications

“…However, there are still several problems in the applications of NN such as the local minimum of network learning, slow convergence rate, complex network structure design and weak generalization performance, and meatheuristic algorithms optimize the parameters of NN to overcome such problems. Jimenez et al discussed a parameter calibration method for random discontinuous network based on GA (Jimenez and Jurado-Pina 2012 ). They present the examples used in original Poisson discontinuous network parameters which are known in advance to verify the proposed method, and the reasoning ability of the model is evaluated with back-calculated parameters and various objective functions with different crossover and mutation probabilities.…”