A Comparison between Adam-Eve GA and Traditional GAs

GAs, which are powerful stochastic optimization techniques are perhaps the most widely known types of evolutionary algorithms. In this paper the Adam-Eve (Like) GA has been considered and its performance has been studied. The basic idea of GAs has been taken from nature; therefore it is reasonable if we expect an improvement in our model of GA, because it is much closer to the evolution of organism. Results provide valuable evidence that proposed Adam-Eve outperforms the traditional GA in both online and offline of the de Jong benchmark functions. Based on the results from an optimization stand point, the Adam-Eve GA seems to produce reasonable engineering results in an inherently difficult area where few other techniques are available.Further more, we made a comparison between Adam-Eve GA and GAVaPS