We've developed an emotional communication robot, WAMOEBA, using behavior-based techniques. We also proposed motor-agent (MA) model, which is an autonomous distributed-control algorithm constructed of simple sensor-motor coordination. Though it enables WAMOEBA to behave in various ways, the weight of the combinations between different motor agents is influenced by the preferences of the developer. We usually use machine-learning algorithms to automatically configure these parameters for communication robots. However, this makes it difficult to define the quantitative evaluation required for communication. We therefore used the method of interactive evolutionary computation (IEC), which can be applied to problems involving quantitative evaluation. IEC does not require to define a fitness function; this task is performed by users. But the biggest problem with using IEC is human fatigue, which causes insufficiency of individuals and generations for convergence of EC. To fix this problem, we use the prediction function that automatically calculates the fitness values of genes from some samples that have received the human subjective evaluation. Then we carried out the behavior acquisition experiment using the IEC simulation system with the prediction function. As the results of experiments, it is confirmed that diversifying the genetic pool is an efficient way for generating a variety of behavior.