Determination of the best crossover operator and ratio in process control approach

Process control is a very important stage in quality control. Conventionally, control charts are used for process control. Kaya and Engin (2007) were proposed a model, which is based on minimum cost and maximum acceptance probability, to determine sample size in control charts for multistage process. In this paper, the model proposed by Kaya and Engin (2007) which is solved aid of genetic algorithms. Generally, Genetic Algorithm (GAs) uses linear binary representations but in this study linear real-valued representation is used. The model is analyzed for the effects of linear real-valued coding systems in GAs. The GAs program is coded in Visual Basic 6.0 and run on a Power Pentium IV (733 MHz). Then five different crossover mechanisms, which are one point, position-based, order, partial-mapped, and linear order crossover are used and their performances are compared with each other. Consequently, the best crossover operator and rate are determined.