Speeding up Composite Differential Evolution for structural optimization using neural networks
Composite Differential Evolution (CoDE) is categorized as a (µ + λ)-Evolutionary Algorithm where each parent produces three trials. Thanks to that, the CoDE algorithm has a strong search capacity. However, the production of many offspring increases the computation cost of fitness evaluation. To over...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-07-01
|
| Series: | Journal of Information and Telecommunication |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/24751839.2021.1946740 |