This paper focuses on an on-board computer (OBC) that evolves computer programs through bit inversion and targets analyzing robustness against bit inversion in registers. We also propose a new method that can change the number of computer programs dynamically. Intensive experiments revealed the following: (1) Correct programs can be maintained even in bit inversion in registers in addition to bit inversion in instructions. (2) Our proposal accelerates program evolution by increasing the population size, i.e., the number of programs, within fixed memory size.

This paper proposes a distributed and self-organized method which has the mechanisms of robustness sensor data collection and fault diagnosis for large-scale sensor networks. To cope with complex sensor networks which include (i) an ad-hoc multi-hop communication, (ii) data aggregation, and (iii) high error data rate caused by the a huge number of nodes, the proposed method collects the information of nodes and diagnoses its nodes by introducing the new nodes called token nodes and the new type of broadcast called a limited virtual broadcast. The token node can create packets which can only be transmitted in the limited multi-hop networks as a limited virtual broadcast, which can prevent a broadcast storm. To clarify the effectiveness of our method, we conduct the simulations on two-hops network and compare the results of our method with those of the conventional method called Adaptive DSD. The intensive simulation results have revealed that our proposed method works well in the large-scale sensor network and shows better performance than Adaptive DSD method from the viewpoint of the communication traffic and rate of information sharing.