This paper discusses the self-adaptive mechanisms of evolution strategies (ES) and real-coded genetic algorithms (RCGA) for optimization in continuous search spaces. For multi-membered evolution strategies, a self-adaptive mechanism of mutation parameters has been proposed by Schwefel. It introduces parameters such as standard deviations of the normal distribution for mutation into the genetic code and lets them evolve by selection as well as the decision variables. In the RCGA, crossover or recombination is used mainly for search. It utilizes information on several individuals to generate novel search points, and therefore, it can generate offspring adaptively according to the distribution of parents without any adaptive parameters. The present paper discusses characteristics of these two self-adaptive mechanisms through numerical experiments. The self-adaptive characteristics such as translation, enlargement, focusing, and directing of the distribution of children generated by the ES and the RCGA are examined through experiments.