Nonlinear optimization for a low-emittance storage ring.

A multi-objective genetic algorithm (MOGA) is a powerful global optimization tool, but its results are considerably affected by the crossover parameter η. Finding an appropriate η demands too much computing time because MOGA needs be run several times in order to find a good η. In this paper, a self-adaptive crossover parameter is introduced in a strategy to adopt a new η for every generation while running MOGA.