A Simple But Effective Evolutionary Algorithm for Complicated Optimization Problems
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 th...
| Main Authors: | , |
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| Format: | Article |
| Language: | en_US |
| Published: |
2003
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| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/4012 |
| _version_ | 1826212459428446208 |
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| author | Xu, Y.G. Liu, Guirong |
| author_facet | Xu, Y.G. Liu, Guirong |
| author_sort | Xu, Y.G. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T15:21:44Z |
| format | Article |
| id | mit-1721.1/4012 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T15:21:44Z |
| publishDate | 2003 |
| record_format | dspace |
| spelling | mit-1721.1/40122019-04-11T08:44:07Z A Simple But Effective Evolutionary Algorithm for Complicated Optimization Problems Xu, Y.G. Liu, Guirong evolutionary algorithm optimization 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. Singapore-MIT Alliance (SMA) 2003-12-23T03:00:41Z 2003-12-23T03:00:41Z 2002-01 Article http://hdl.handle.net/1721.1/4012 en_US High Performance Computation for Engineered Systems (HPCES); 65862 bytes application/pdf application/pdf |
| spellingShingle | evolutionary algorithm optimization Xu, Y.G. Liu, Guirong A Simple But Effective Evolutionary Algorithm for Complicated Optimization Problems |
| title | A Simple But Effective Evolutionary Algorithm for Complicated Optimization Problems |
| title_full | A Simple But Effective Evolutionary Algorithm for Complicated Optimization Problems |
| title_fullStr | A Simple But Effective Evolutionary Algorithm for Complicated Optimization Problems |
| title_full_unstemmed | A Simple But Effective Evolutionary Algorithm for Complicated Optimization Problems |
| title_short | A Simple But Effective Evolutionary Algorithm for Complicated Optimization Problems |
| title_sort | simple but effective evolutionary algorithm for complicated optimization problems |
| topic | evolutionary algorithm optimization |
| url | http://hdl.handle.net/1721.1/4012 |
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