A Clustering Approach for Evaluation of Slope Stability Based on Genetic Algorithm
A clustering method for evaluation of slope stability is developed based on genetic algorithm. By incorporating features of the problem discussed, the corresponding genetic operators such as selection strategy, crossover operator, and mutation operator are designed to promote global search. Computational results show that the GA-based model can avoid the disadvantages of ordinary clustering methods and find the optimum solution easily with no prior knowledge about data's distributions.