Figure 9. Schematic lists the primary components of the cw-positron test bed; (1) 10 MeV polarized electron beam, (2) shield hut, (3) pair-creation target, (4) collection solenoid and (5) diagnostic suite. Beam physics intrinsically deals with sophisticated systems of many variables and their specifications. The Genetic Algorithm is a powerful method that implements principles employed in biological evolution to optimize non-linear problems. Introduced into accelerator physics relatively recently (1992) the Multi-Object Genetic Algorithm (MOGA) has been increasingly employed, first for optimizing the design of magnets and RF cavities, and later in increasingly sophisticated problems, such as determining the operating parameters to achieve the highest brightness high-current electron photo-injector ever, at Cornell University [BA05-1], to optimize luminosity in the International Linear Collider design [BA05-2] and to optimize design and operating costs for an SRF linac [BA05-3]. The strength of this technique with respect to the proposed work is the capability to globally judge desirable or dominant traits such as positron yield, beam brightness (yield/emittance), figure of merit (yield x polarization2), and so on. While some parameters readily optimize (e.g. one may always benefit from an electron drive beam with highest polarization), it is possible with a large parameter base for design-bias or local optimization (meaning, a subset of parameters in the system) to mislead.
Figure 9. Schematic lists the primary components of the cw-positron test bed; (1) 10 MeV polarized electron beam, (2) shield hut, (3) pair-creation target, (4) collection solenoid and (5) diagnostic suite. Beam physics intrinsically deals with sophisticated systems of many variables and their specifications. The Genetic Algorithm is a powerful method that implements principles employed in biological evolution to optimize non-linear problems. Introduced into accelerator physics relatively recently (1992) the Multi-Object Genetic Algorithm (MOGA) has been increasingly employed, first for optimizing the design of magnets and RF cavities, and later in increasingly sophisticated problems, such as determining the operating parameters to achieve the highest brightness high-current electron photo-injector ever, at Cornell University [BA05-1], to optimize luminosity in the International Linear Collider design [BA05-2] and to optimize design and operating costs for an SRF linac [BA05-3]. The strength of this technique with respect to the proposed work is the capability to globally judge desirable or dominant traits such as positron yield, beam brightness (yield/emittance), figure of merit (yield x polarization2), and so on. While some parameters readily optimize (e.g. one may always benefit from an electron drive beam with highest polarization), it is possible with a large parameter base for design-bias or local optimization (meaning, a subset of parameters in the system) to mislead.