Continuous optimization using elite genetic algorithms with adaptive mutations
Апстракт
The elite genetic algorithm with adaptive mutations is applied to two different continuous optimization problems: determination of model parameters of optical constants of aluminum and thin film optical filter design. The concept of adaptive mutations makes the employed algorithm a versatile tool for solving continuous optimization problems. The algorithm has been successful in solving both investigated problems. In determination of optical constants of aluminum, excellent agreement between calculated and experimental data is obtained. In application to thin film optical filter design, low-pass filters designed using this algorithm are clearly superior to filters designed using the traditional approach.
Извор:
Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence, 1999, 1585, 365-372Напомена:
- 2nd Asia-Pacific Conference on Simulated Evolution and Learning (SEAL 98), Nov 24-27, 1998, Canberra, Australia
Колекције
Институција/група
VinčaTY - JOUR AU - Djurisic, AB AU - Rakić, AD AU - Li, EH AU - Majewski, ML AU - Bundaleski, Nenad AU - Stanić, Božidar V. PY - 1999 UR - https://vinar.vin.bg.ac.rs/handle/123456789/6288 AB - The elite genetic algorithm with adaptive mutations is applied to two different continuous optimization problems: determination of model parameters of optical constants of aluminum and thin film optical filter design. The concept of adaptive mutations makes the employed algorithm a versatile tool for solving continuous optimization problems. The algorithm has been successful in solving both investigated problems. In determination of optical constants of aluminum, excellent agreement between calculated and experimental data is obtained. In application to thin film optical filter design, low-pass filters designed using this algorithm are clearly superior to filters designed using the traditional approach. T2 - Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence T1 - Continuous optimization using elite genetic algorithms with adaptive mutations VL - 1585 SP - 365 EP - 372 UR - https://hdl.handle.net/21.15107/rcub_vinar_6288 ER -
@article{ author = "Djurisic, AB and Rakić, AD and Li, EH and Majewski, ML and Bundaleski, Nenad and Stanić, Božidar V.", year = "1999", abstract = "The elite genetic algorithm with adaptive mutations is applied to two different continuous optimization problems: determination of model parameters of optical constants of aluminum and thin film optical filter design. The concept of adaptive mutations makes the employed algorithm a versatile tool for solving continuous optimization problems. The algorithm has been successful in solving both investigated problems. In determination of optical constants of aluminum, excellent agreement between calculated and experimental data is obtained. In application to thin film optical filter design, low-pass filters designed using this algorithm are clearly superior to filters designed using the traditional approach.", journal = "Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence", title = "Continuous optimization using elite genetic algorithms with adaptive mutations", volume = "1585", pages = "365-372", url = "https://hdl.handle.net/21.15107/rcub_vinar_6288" }
Djurisic, A., Rakić, A., Li, E., Majewski, M., Bundaleski, N.,& Stanić, B. V.. (1999). Continuous optimization using elite genetic algorithms with adaptive mutations. in Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence, 1585, 365-372. https://hdl.handle.net/21.15107/rcub_vinar_6288
Djurisic A, Rakić A, Li E, Majewski M, Bundaleski N, Stanić BV. Continuous optimization using elite genetic algorithms with adaptive mutations. in Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence. 1999;1585:365-372. https://hdl.handle.net/21.15107/rcub_vinar_6288 .
Djurisic, AB, Rakić, AD, Li, EH, Majewski, ML, Bundaleski, Nenad, Stanić, Božidar V., "Continuous optimization using elite genetic algorithms with adaptive mutations" in Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence, 1585 (1999):365-372, https://hdl.handle.net/21.15107/rcub_vinar_6288 .