Repository at Hanyang UniversityCOLLEGE OF ENGINEERING SCIENCES[E](공학대학)ELECTRICAL ENGINEERING(전자공학부)Articles
123
0
Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Jun Zhang | - |
| dc.date.accessioned | 2024-01-08T02:43:29Z | - |
| dc.date.available | 2024-01-08T02:43:29Z | - |
| dc.date.issued | 2023-12 | - |
| dc.identifier.citation | IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, v. 27, NO 6, Page. 1794-1808 | en_US |
| dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/188048 | - |
| dc.description | Learning and optimization are the two essential abilities of human beings for problem solving. Similarly, computer scientists have made great efforts to design artificial neural network (ANN) and evolutionary computation (EC) to simulate the learning ability and the optimization ability for solving real-world problems, respectively. These have been two essential branches in artificial intelligence (AI) and computer science. However, in humans, learning and optimization are usually integrated together for problem solving. Therefore, how to efficiently integrate these two abilities together to develop powerful AI remains a significant but challenging issue. Motivated by this, this article proposes a novel learning-aided evolutionary optimization (LEO) framework that plus learning and evolution for solving optimization problems. The LEO is integrated with the evolution knowledge learned by ANN from the evolution process of EC to promote optimization efficiency. The LEO framework is applied to both classical EC algorithms and some state-of-the-art EC algorithms including a champion algorithm, with benchmarking against the IEEE Congress on EC competition data. The experimental results show that the LEO can significantly enhance the existing EC algorithms to better solve both single-objective and multi-/many-objective global optimization problems, suggesting that learning plus evolution is more intelligent for problem solving. Moreover, the experimental results have also validated the time efficiency of the LEO, where the additional time cost for using LEO is greatly deserved. Therefore, the promising LEO can lead to a new and more efficient paradigm for EC algorithms to solve global optimization problems by plus learning and evolution. | en_US |
| dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | en_US |
| dc.title | Learning-aided Evolution for Optimization | en_US |
| dc.type | Article | en_US |
| dc.relation.no | 6 | - |
| dc.relation.volume | 27 | - |
| dc.relation.page | 1794-1808 | - |
| dc.relation.journal | IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION | - |
| dc.relation.code | 2023041207 | - |
| dc.sector.campus | E | - |
| dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
| dc.sector.department | SCHOOL OF ELECTRICAL ENGINEERING | - |
| dc.identifier.pid | junzhanghk | - |
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
- Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
