Evaluation of the different genetic algorithm parameters and operators for the finite element model updating problem

Erdogan Y. S., BAKIR P. G.

COMPUTERS AND CONCRETE, vol.11, no.6, pp.541-569, 2013 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 11 Issue: 6
  • Publication Date: 2013
  • Doi Number: 10.12989/cac.2013.11.6.541
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.541-569
  • Istanbul Technical University Affiliated: No


There is a wide variety of existing Genetic Algorithms (GA) operators and parameters in the literature. However, there is no unique technique that shows the best performance for different classes of optimization problems. Hence, the evaluation of these operators and parameters, which influence the effectiveness of the search process, must be carried out on a problem basis. This paper presents a comparison for the influence of GA operators and parameters on the performance of the damage identification problem using the finite element model updating method (FEMU). The damage is defined as reduction in bending rigidity of the finite elements of a reinforced concrete beam. A certain damage scenario is adopted and identified using different GA operators by minimizing the differences between experimental and analytical modal parameters. In this study, different selection, crossover and mutation operators are compared with each other based on the reliability, accuracy and efficiency criteria. The exploration and exploitation capabilities of different operators are evaluated. Also a comparison is carried out for the parallel and sequential GAs with different population sizes and the effect of the multiple use of some crossover operators is investigated. The results show that the roulettewheel selection technique together with real valued encoding gives the best results. It is also apparent that the Non-uniform Mutation as well as Parent Centric Normal Crossover can be confidently used in the damage identification problem. Nevertheless the parallel GAs increases both computation speed and the efficiency of the method.