**7. Conclusion**

We have introduced a probabilistic program evolution algorithm named PAGE and its extension UPAGE. PAGE takes advantage of latent annotations that enables consideration of dependencies among nodes, and UPAGE incorporates a mixture model for taking into account global contexts. By applying UPAGE to computational experiments, we have confirmed that a mixture model is highly effective for obtaining solutions in terms of the number of fitness evaluations. At the same time, UPAGE is more advantageous than PAGE in the sense that UPAGE can obtain multiple solutions for multimodal problems. We hope that it will be possible to apply PAGE and UPAGE to a wide class of real-world problems, which is an intended future area of study.
