Genetic algorithms are highly parallel, adaptive search method based on the processes of Darwinian evolution. This paper combines genetic algorithms with simulated annealing algorithms to a new kind of random search algorithms which is called genetic evolution algorithms. We give some conditions which guarantee random search algorithms to converge to the global optima set with probability 1 for solving optimization problems and analyze the convergence of genetic evolution algorithms by using Markov chain theory.