Investigations in meta-GAs: panaceas or pipe dreams?
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Robustness of Isotropic Stable Mutations in a General Search Space
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Reinforcement learning for online control of evolutionary algorithms
ESOA'06 Proceedings of the 4th international conference on Engineering self-organising systems
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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: 1) user-determined choices among alternative parameters, operations, and logic structures, and 2) the algorithm-determined alternative paths chosen during the process of seeking a solution to a particular problem. This paper discusses 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.