Real-coded genetic algorithms (RCGAs) have proved to be more efficient than traditional bit-string genetic algorithms (GAs) in parameter optimization, but a RCGA focuses more on crossover operators and less on mutation operators for local searching. Evolution strategies (ESs) and evolutionary programming (EP) only concern the Gaussian mutation operators. This paper proposes a technique called a combined evolutionary algorithm (CEA) by incorporating the ideas of EP and GAs into an ES. Simultaneously, we add local competition into the CEA in order to reduce the complexity and maintain diversity. More than 20 different function optimization problems are taken as benchmark problems. The results indicate that the CEA approach is a very powerful optimization technique.