Name
Abstract | ||
|---|---|---|
We propose a performance enhancement using parallelization of genetic operations that takes highly fit schemata (building-block) linkages into account. Previously, we used the problem of solving Sudoku puzzles to demonstrate the possibility of shortening processing times through the use of many-core processors for genetic computations. To increase accuracy, we proposed a genetic operation that takes building-block linkages into account. Here, in an evaluation using very difficult problems, we show that the proposed genetic operations are suited to fine-grained parallelization; processing performance increased by approximately 30 % (four times) with fine-grained parallel processing of the proposed mutation and crossover methods on Intel Core i5 (NVIDIA GTX5800) compared with non-parallel processing on a CPU. Increasing GPU resources will diminish the conflicts with thread usage in coarse-grained parallelization of individuals and will enable faster processing. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/s10015-012-0012-x | Artificial Life and Robotics |
Keywords | DocType | Volume |
building-block linkage,processing time,faster processing,genetic operation,proposed genetic operation,non-parallel processing,fine-grained parallel processing,coarse-grained parallelization,genetic computation,processing performance,fine-grained parallelization,parallelization,genetic algorithms,linkage | Journal | 17 |
Issue | ISSN | Citations |
1 | 1614-7456 | 1 |
PageRank | References | Authors |
0.48 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuji Sato | 1 | 48 | 18.14 |
| Hazuki Inoue | 2 | 3 | 0.88 |
| Mikiko Sato | 3 | 22 | 11.53 |