Name
Abstract | ||
|---|---|---|
This paper suggests a new genetic algorithm (GA) for VLSI circuit partitioning problem. In a genetic algorithm, the encoding of a solution plays an important role. The key feature of the new genetic algorithm is a technique to provide dynamically many encodings in which encodings themselves undergo evolution. Before generating every new solution, we first generate a new encoding by combining two encodings chosen from a pool containing diverse encodings, The new solution is generated by a crossover which combines two parent solutions which are temporarily encoded by the generated encoding scheme. That is, a new solution is generated by a two-layered crossover. Depending on dhe new solution's quality and its improvement over the parents solutions, a fitness value is assigned to the underlying encoding. The encoding is discarded or enter the pool based an the fitness. Two populations are maintained for this purpose: one for solutions and the other for diverse encodings, On experiments with the public ACM/SIGDA benchmark circuits, the new genetic algorithm significantly outperformed recently published state-of-the-art approaches. |
Year | DOI | Venue |
|---|---|---|
1997 | 10.1109/KES.1997.619424 | FIRST INTERNATIONAL CONFERENCE ON KNOWLEDGE-BASED INTELLIGENT ELECTRONIC SYSTEMS, PROCEEDINGS 1997 - KES '97, VOLS 1 AND 2 |
Keywords | Field | DocType |
moon,very large scale integration,crossover,computer science,routing,genetic algorithm,genetics,integrated circuit design,vlsi,genetic algorithms,encoding,sparse matrices | Embedding,Crossover,Computer science,Algorithm,Theoretical computer science,Integrated circuit design,Electronic circuit,Very-large-scale integration,Sparse matrix,Genetic algorithm,Encoding (memory) | Conference |
Citations | PageRank | References |
1 | 0.37 | 15 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Byung Ro Moon | 1 | 366 | 31.12 |
| Chun-kyung Kim | 2 | 19 | 1.95 |