The extensible genetic optimizer (EGO) is a state-of-the-art evolutionary computation optimizer . This optimizer is based on a genetic algorithm which has been developed especially for TCAD demands, where computationally expensive score functions have to be evaluated. The optimizer EGO provides a GAUSSian mutation operator, which changes for instance to min(max( ), where is a GAUSSian distribution function and the standard deviation depends on the interval length. The crossover operators available in EGO are the linear randomized crossover, the two-point crossover, and the uniform crossover operators. Constraints can be considered as penalty terms in the score function, which usually works not very well due to the reduced convergence property.