Name
Abstract | ||
|---|---|---|
Scalability is a main and urgent problem in evolvable hardware (EHW) field. For the design of large circuits, an EHW method with a decomposition strategy is able to successfully find a solution, but requires a large complexity and evolution time. This study aims to optimize the decomposition on large-scale circuits so that it provides a solution for the EHW method to scalability and improves the efficiency. This paper proposes a projection-based decomposition (PD), together with Cartesian genetic programming (CGP) as an EHW system namely PD-CGP, to design relatively large circuits. PD gradually decomposes a Boolean function by adaptively projecting it onto the property of variables, which makes the complexity and number of sub-logic blocks minimized. CGP employs an evolutionary strategy to search for the simple and compact solutions of these sub-blocks. The benchmark circuits from the MCNC library, $$n$$n-parity circuits, and arithmetic circuits are used in the experiment to prove the ability of PD-CGP in solving scalability and efficiency. The results illustrate that PD-CGP is superior to 3SD-ES in evolving large circuits in terms of complexity reduction. PD-CGP also outperforms GDD+GA in evolving relatively large arithmetic circuits. Additionally, PD-CGP successfully evolves larger $$n$$n-even-parity and arithmetic circuits, which have not done by other approaches. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s00500-015-1636-2 | Soft Computing - A Fusion of Foundations, Methodologies and Applications |
Keywords | Field | DocType |
EHW, Scalability, Circuit evolution, Projection, Decomposition, Cartesian genetic programming | Boolean function,Computer science,Evolvable hardware,Theoretical computer science,Cartesian genetic programming,Evolution strategy,Artificial intelligence,Arithmetic circuits,Mathematical optimization,Reduction (complexity),Electronic circuit,Machine learning,Scalability | Journal |
Volume | Issue | ISSN |
20 | 6 | 1432-7643 |
Citations | PageRank | References |
3 | 0.40 | 26 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanyun Tao | 1 | 6 | 2.15 |
| Lijun Zhang | 2 | 245 | 37.10 |
| Yuzhen Zhang | 3 | 14 | 3.99 |