Relationship between problem characteristics and the optimal number of genetic algorithm generations

Genetic Algorithms (GAs) have been successfully applied to a wide range of engineering optimization problems. The success of the GA is dependent on the parameter values used, and identifying suitable values to use is a difficult task. Typically, the GA parameters must be calibrated for each application, hence it might be expected that the optimal parameter values are related to the characteristics of each problem. To aid the calibration of GAs, it is proposed that there exists an optimal number of GA generations for a given problem, where from the number of generations, the population size can be determined from the total function evaluations that are available. A number of test functions have been considered with different problem characteristics, such as salience, correlation structure, and epistasis, and statistics are proposed to quantify each of these characteristics. A large-scale parametric study has been undertaken to determine the effect of these characteristics on the optimal number of GA generations necessary in order to solve the problem most efficiently. From these results, two function classes have been identified. The function classes have been tested on two instances of an engineering optimization problem and found to classify the most appropriate population size accurately for solving each problem. Hence, the classification method developed can be used to assist in the calibration of GAs for applications where long simulation times are expected.