Genetic search with dynamic operating disciplines

This study extends the idea of genetic search to include the selection of specific operating procedures and disciplines during the course of evolution. Operating disciplines are allowed to change and evolve to better suit the search effort. The two major benefits anticipated are performance and robustness. Performance is measured by the goodness of the solution and by the computational effort required to obtain the solution. Robustness is related to the performance of the methodology for a wide range of optimization problems. A robust methodology is one which is not adversely affected by a prespecified operating discipline parameter. In this perspective, robustness is related to the generality of the methodology: its ability to perform well even when the search effort starts in a state not suitable for the specific conditions of the problem at hand. The methodology is demonstrated and evaluated by implementing a known-to-be-difficult class of scheduling problems, the single-machine deterministic scheduling problems in which the objective is to minimize both the earliness and the tardiness.