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TY - CONF AU - van der Geer, S.B. AU - de Loos, M.J. ED - Henderson, Stuart ED - Akers, Evelyn ED - Satogata, Todd ED - Schaa, Volker R.W. TI - Multi-objective Genetic Optimization with the General Particle Tracer (GPT) Code J2 - Proc. of IPAC2015, Richmond, VA, USA, May 3-8, 2015 C1 - Richmond, VA, USA T2 - International Particle Accelerator Conference T3 - 6 LA - english AB - In a typical design process there are a large number of variables, external constraints, and multiple conflicting objectives. Examples of the latter are short pulse, high charge, low emittance and low price. The classical solution to handle such problems is to combine all objectives into one merit function. This however implicitly assumes that the tradeoffs between all objectives are a-priori known. Especially in the early design stages this is hardly ever the case. A popular solution to this problem is to switch to multi-objective genetic optimization algorithms. This class of algorithms solves the problem by genetically optimising an entire population of sample solutions. Selection and recombination operators are defined such that the output, the so-called Pareto front, only includes solutions that are fully optimized where no objective can be improved without degrading any other. Here we present numerical studies and practical test runs of the genetic optimizer built into the General Particle Tracer (GPT) code. PB - JACoW CP - Geneva, Switzerland SP - 492 EP - 494 KW - solenoid KW - emittance KW - factory KW - cavity KW - target DA - 2015/06 PY - 2015 SN - 978-3-95450-168-7 DO - 10.18429/JACoW-IPAC2015-MOPJE076 UR - http://jacow.org/ipac2015/papers/mopje076.pdf ER -