Evolutionary Algorithms (EAs) have recently been successfully applied to numerical optimization problems. A major obstacle in the application of EAs has been the relatively slow convergence rate. This becomes more pronounced when the functions to be optimized become complex and numerically intensive. In this paper five different methods of speeding up EA convergence are reviewed. These include Classical Evolutionary Programming (CEP) with a Gaussian mutation operator, Fast Evolutionary Programming (FEP) with a Cauchy mutation operator, Adaptive Lévy Mutation with a Lévy mutation operator, and the combined mutation operator strategies of Mean Mutation Operator (MMO) and Adaptive Mean Mutation Operator (AMMO). These five methods are compared on a set of benchmark functions. Finally, in this paper an example of a complex and numerically intensive optimization problem is demonstrated by maximizing the output current of an automotive alternator. In this demonstration FEP will be applied to a Magnetic Equivalent Circuit (MEC) model of the alternator with the purpose of maximizing output current.