Sensitivity Analysis to Determine the Parameters of Genetic Algorithm for Machine Layout

AbstractWhen Genetic Algorithms (GA) are used to solve layout problems, the solution quality may be influenced by the population size, number of generations, the rate of crossover, the rate of mutation, and the length of the block to be exchanged between parents to generate offspring's. These parameters have been used with different values under given environments. Usually, the user selects the values of these parameters with no guidelines as what values might work better. Therefore, the sensitivity analysis approach has to be followed to establish the guidelines for fixing the values of the parameters and to study any possible effect of these parameters and their interaction on the quality of the solution. Hence, the values of the GA parameters that produce quality solutions have to be identified. This study is particularly important when real-world problems are to be solved, where the knowledge about their optimal solutions does not exist. Therefore, a guideline to select the GA parameters has to be established. The genetic algorithm model, the most suitable way of coding the solutions into the organisms and the selected evolutionary and genetic operators are presented. In this connection the most favorable parameters for GA are found out by sensitivity analysis for a machine layout problem