| dc.contributor.author | Xu, Y.G. | |
| dc.contributor.author | Liu, Guirong | |
| dc.date.accessioned | 2003-12-23T03:00:41Z | |
| dc.date.available | 2003-12-23T03:00:41Z | |
| dc.date.issued | 2002-01 | |
| dc.identifier.uri | http://hdl.handle.net/1721.1/4012 | |
| dc.description.abstract | A simple but effective evolutionary algorithm is proposed in this paper for solving complicated optimization problems. The new algorithm presents two hybridization operations incorporated with the conventional genetic algorithm. It takes only 4.1% ~ 4.7% number of function evaluations required by the conventional genetic algorithm to obtain global optima for the benchmark functions tested. Application example is also provided to demonstrate its effectiveness. | en |
| dc.description.sponsorship | Singapore-MIT Alliance (SMA) | en |
| dc.format.extent | 65862 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en_US | |
| dc.relation.ispartofseries | High Performance Computation for Engineered Systems (HPCES); | |
| dc.subject | evolutionary algorithm | en |
| dc.subject | optimization | en |
| dc.title | A Simple But Effective Evolutionary Algorithm for Complicated Optimization Problems | en |
| dc.type | Article | en |
