Development of a Mutation Operator in a Real-Coded Genetic Algorithm for Bridge Model Optimization

A mutation operator in a real-coded genetic algorithm is developed and applied for efficient bridge-model optimization. A mutation operator that changes uniformly or dynamically with a crossover operator is proposed to address optimization problems. The performance of the combined genetic operators was verified using a variety of available test problems based on the convergence and search speed of the global optimal solution. It is shown that the genetic algorithm proposed in this study yields relatively better results than the available algorithms and is more effective in constrained optimization problems. The performance of the proposed genetic algorithm is also verified through a sample study using a field load test for the model optimization of an existing bridge.