Outline for a Logical Theory of Adaptive Systems
Journal of the ACM (JACM)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
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Undulator magnets shimming is a stage of the undulator production cycle that has a major influence on the quality of a synchrotron radiation. Despite the high complexity of the underlying decision making process, shimming is traditionally performed in a semi-empirical way using a high level expert knowledge. Such a labor intensive approach introduces additional cost and delay in undulator production. The present study introduces an automated decision making procedure for shimming based on an appropriate formalization of this task. Our approach consists in formulating the corresponding constrained optimization problem, which can be efficiently solved by an evolutionary algorithm using 3D magnetostatic methods and magnetic measurement for fitness calculation. Such automation allows us to reduce the time and cost of the undulator production and it leads to results of a quality level that is hardly attainable empirically considering the high complexity of the optimization problem.