Efficient reinforcement learning through symbiotic evolution
Machine Learning - Special issue on reinforcement learning
Node-covering, Error-correcting Codes and Multiprocessors with Very High Average Fault Tolerance
IEEE Transactions on Computers
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Searching for diverse, cooperative populations with genetic algorithms
Evolutionary Computation
Using genetic algorithms to explore pattern recognition in the immune system
Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Genetic reinforcement learning through symbiotic evolution forfuzzy controller design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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A novel immunotronic approach to fault detection in hardware based on symbiotic evolution is proposed in this paper. In the immunotronic system, the generation of tolerance conditions corresponds to the generation of antibodies in the biological immune system. In this paper, the principle of antibody diversity, one of the most important concepts in the biological immune system, is employed and it is realized through symbiotic evolution. Symbiotic evolution imitates the generation of antibodies in the biological immune system more than the standard genetic algorithm(SGA) does. It is demonstrated that the suggested method outperforms the previous immunotronic methods with less running time. The suggested method is applied to fault detection in a decade counter (typical example of finite state machines) and MCNC finite state machines and its effectiveness is demonstrated by the computer simulation.