No, actually genetic algorithms are parametric models (<a href="http://en.wikipedia.org/wiki/Parametric_model" rel="nofollow">http://en.wikipedia.org/wiki/Parametric_model</a>). They cannot get results than is permitted by their model class. And they are relatively poor optimization methods for exploring their parameter space. I cannot think of a single genetic algorithm work published recently in a top-notch machine learning conference (NIPS, ICML, etc.)