Abstract
Jumping gene adaptation of differential evolution algorithm (DE-JGa) is proposed in this study. Detailed working principle of proposed algorithm is given. The performance of the proposed algorithm (DE-JGa) is compared with the performance of existing algorithms (differential evolution (DE) and modified differential evolution (MDE)] by applying these algorithms to some selected benchmark test problems. Thorough algorithm specific study is carried out to check the robustness of individual algorithms with respect to its control parameters, such as crossover constant (CR), number of population points (NP), scaling factor (F) and the number of generations.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 5th Indian International Conference on Artificial Intelligence, IICAI 2011 |
| Pages | 353-370 |
| Number of pages | 18 |
| Publication status | Published - 2011 |
| Event | 5th Indian International Conference on Artificial Intelligence, IICAI 2011 - Tumkur, India Duration: Dec 14 2011 → Dec 16 2011 |
Other
| Other | 5th Indian International Conference on Artificial Intelligence, IICAI 2011 |
|---|---|
| Country | India |
| City | Tumkur |
| Period | 12/14/11 → 12/16/11 |
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Keywords
- Differential evolution
- Evolutionary algorithms
- Jumping gene
- Optimization
ASJC Scopus subject areas
- Artificial Intelligence
Cite this
Differential evolution using jumping genes adaptation. / Gujarathi, Ashish M.; Purohit, Saumitra; Manchanda, Rohan; Ghosh, Debabrata.
Proceedings of the 5th Indian International Conference on Artificial Intelligence, IICAI 2011. 2011. p. 353-370.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Differential evolution using jumping genes adaptation
AU - Gujarathi, Ashish M.
AU - Purohit, Saumitra
AU - Manchanda, Rohan
AU - Ghosh, Debabrata
PY - 2011
Y1 - 2011
N2 - Jumping gene adaptation of differential evolution algorithm (DE-JGa) is proposed in this study. Detailed working principle of proposed algorithm is given. The performance of the proposed algorithm (DE-JGa) is compared with the performance of existing algorithms (differential evolution (DE) and modified differential evolution (MDE)] by applying these algorithms to some selected benchmark test problems. Thorough algorithm specific study is carried out to check the robustness of individual algorithms with respect to its control parameters, such as crossover constant (CR), number of population points (NP), scaling factor (F) and the number of generations.
AB - Jumping gene adaptation of differential evolution algorithm (DE-JGa) is proposed in this study. Detailed working principle of proposed algorithm is given. The performance of the proposed algorithm (DE-JGa) is compared with the performance of existing algorithms (differential evolution (DE) and modified differential evolution (MDE)] by applying these algorithms to some selected benchmark test problems. Thorough algorithm specific study is carried out to check the robustness of individual algorithms with respect to its control parameters, such as crossover constant (CR), number of population points (NP), scaling factor (F) and the number of generations.
KW - Differential evolution
KW - Evolutionary algorithms
KW - Jumping gene
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=84872177129&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872177129&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84872177129
SN - 9780972741286
SP - 353
EP - 370
BT - Proceedings of the 5th Indian International Conference on Artificial Intelligence, IICAI 2011
ER -