This paper describes an evolutionary algorithm based on a statistical representation of populations of individuals. Experiments on robot navigation and on numerical fitness functions are presented in order to measure the performance of the algorithm compared to traditional genetic algorithms. Results show that the method is suitable for onboard online evolution because it requires low amount of memory resources. Furthermore, it allows for incremental evolution in dynamic environments in order to cope with complex tasks that require several evolutionary stages.