Toward the optimization of a class of black box optimization algorithms
Gang Wang, Erik D. Goodman, William F. Punch · 2002
Many black box optimization algorithms have sufficient flexibility to allow them to adapt to the varying circumstances they encounter. These capabilities are of two primary sorts: user-determined choices among alternative parameters, operations, and logic structures; and the algorithm-determined alternative paths chosen during the process of seeking a solution to a particular problem. We discuss the process of algorithm design and operation, with the intent of integrating the seemingly distinct aspects described above within a unified framework. We relate this algorithmic optimization process to the field of dynamic process control. An approach is proposed toward the optimization of a process for controlling a specific class of systems, and its application to dynamic adjustment of the algorithm used in the search problem. An instance of this approach in genetic algorithms is demonstrated. The experimental results show the adaptability and robustness of the proposed approach.