SLAC, Menlo Park, California, USA
The genetic algorithm (GA) has been a popular technique in optimizing the design and operation of particle accelerators. As a population based algorithm, GA requires a large amount of evaluations of the objective functions, which can be very time consuming. One can benefit from parallel computing with significantly reduced computing time when fulfilling the function evaluation by a numerical machine model in simulation codes. As a result, this is the most common approach in GA applications. In this paper, we present a successful experimental demonstration of applying the GA in real machine based optimization. We conduct the optimization of the linear coupling of the SPEAR3 storage ring using the GA by directly varying the strengths of SPEAR 3 skew quadrupoles, the decision variables, and measuring the beam loss rates, the sole objective function. The results in this paper can shed light on new applications of GAs in particle accelerator community.
