Nowadays, Robots account for most part of our lives in such a way that it is impossible for usto do many of affairs without them. Increasingly, the application of robots is developing fastand their functions become more sensitive and complex. One of the important requir
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Nowadays, Robots account for most part of our lives in such a way that it is impossible for usto do many of affairs without them. Increasingly, the application of robots is developing fastand their functions become more sensitive and complex. One of the important requirements ofRobot use is a reliable software operation. For enhancement of reliability, it is a necessity todesign the fault tolerance system. In this paper, we will present a genetic algorithm andlearning automata with high reliability to evaluate the software designed into the robotagainst soft-error with minimum performance over-head. This method relies on experiment;hence, we use the program sets as criteria in evaluation stages. Indeed, we have used the errorinjection method in the execution of experimental processes. Relevant data, regardingprogram execution behavior were collected and then analyzed. To evaluate the behavior ofprogram, errors entered using the simple scalar simulation software.
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