Balancing Samples' Contributions on GA Learning
ICES '01 Proceedings of the 4th International Conference on Evolvable Systems: From Biology to Hardware
Evolutionary Algorithms and Theirs Use in the Design of Sequential Logic Circuits
Genetic Programming and Evolvable Machines
Improving FSM evolution with progressive fitness functions
Proceedings of the 14th ACM Great Lakes symposium on VLSI
A more bio-plausible approach to the evolutionary inference of finite state machines
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
A three-step decomposition method for the evolutionary design of sequential logic circuits
Genetic Programming and Evolvable Machines
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Our previous work focused on the synthesis of sequential circuits based on a partial input/output sequence. As the behavioural description of the target circuit is not known the correctness of the result can not be verified. This paper proposes a method which increases the correctness percentage of the finite-state machine (FSM) synthesis using multiple partial input/output sequences. The synthesizer is based on Genetic Algorithm. The experimental results show that the correctness percentage can be increased to 100% by increasing of the number of input/output sequences.