Abstract
The main difficulty in the evolutionary design of finite state machines (FSMs) is lack of effective systematic EHW approach. To accomplish the evolutionary design of FSMs, a systematic EHW method named genetic programming---evolutionary strategy (GP---ES), which is a combination of ES and GP, is proposed. ES optimizes the state assignment and provide them to GP for population generation; GP is responsible for evolving the combinational part of FSM, and feeding the fitness of population back to ES for the evaluation of corresponding state assignments. GP---ES is tested extensively on twenty FSMs from MCNC Library. The results demonstrate that the GP---ES-derived state assignments are more efficient than the ones of Xia, Ali, Almaini and NOVA in the evolutionary design of FSMs. The results also illustrate that the GP---ES is superior to conventional synthesis tools in terms of complexity reduction for the design of small and middle FSMs. GP---ES also performs well in comparison with 3SD-ES in most cases.
Published Version
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