Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s00500-015-1791-5 | Soft Comput. |
Keywords | Field | DocType |
Sequential circuit, State assignment, FSM, EHW method, Evolutionary strategy, Genetic programming | Population,Sequential logic,Computer science,Genetic programming,Reduction (complexity),Theoretical computer science,Finite-state machine,Evolution strategy,Artificial intelligence | Journal |
Volume | Issue | ISSN |
20 | 12 | 1433-7479 |
Citations | PageRank | References |
1 | 0.35 | 25 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanyun Tao | 1 | 6 | 2.15 |
| Qing Zhang | 2 | 1 | 0.69 |
| Lijun Zhang | 3 | 245 | 37.10 |
| Yuzhen Zhang | 4 | 14 | 3.99 |