Abstract
The rapid increase of computer power enabled us to solve many real world problems using optimization algorithms. However, the recent novel metaheuristic algorithms do not stand on concrete ground like traditional algorithms did. Therefore, there should be many rooms to improve those algorithms, and one of the way is to alter the formula of an algorithm. In this study, we define algorithm designing as an optimization problem, and use genetic programming to find new mutation schemes for differential evolution. In addition, we evaluate the generated mutation schemes using several benchmarks to verify that the proposed methods and the generated algorithms are effective ones.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the SICE Annual Conference |
| Pages | 1548-1549 |
| Number of pages | 2 |
| Publication status | Published - 2013 |
| Event | 2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013 - Nagoya, Japan Duration: 2013 Sep 14 → 2013 Sep 17 |
Other
| Other | 2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013 |
|---|---|
| Country | Japan |
| City | Nagoya |
| Period | 13/9/14 → 13/9/17 |
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Keywords
- Differential evolution
- Evolutionary algorithm designing
- Genetic programming
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Computer Science Applications
Cite this
Designing optimal updating rule for differential evolution using genetic programming. / Kanemasa, Minoru; Aiyoshi, Eitaro.
Proceedings of the SICE Annual Conference. 2013. p. 1548-1549.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Designing optimal updating rule for differential evolution using genetic programming
AU - Kanemasa, Minoru
AU - Aiyoshi, Eitaro
PY - 2013
Y1 - 2013
N2 - The rapid increase of computer power enabled us to solve many real world problems using optimization algorithms. However, the recent novel metaheuristic algorithms do not stand on concrete ground like traditional algorithms did. Therefore, there should be many rooms to improve those algorithms, and one of the way is to alter the formula of an algorithm. In this study, we define algorithm designing as an optimization problem, and use genetic programming to find new mutation schemes for differential evolution. In addition, we evaluate the generated mutation schemes using several benchmarks to verify that the proposed methods and the generated algorithms are effective ones.
AB - The rapid increase of computer power enabled us to solve many real world problems using optimization algorithms. However, the recent novel metaheuristic algorithms do not stand on concrete ground like traditional algorithms did. Therefore, there should be many rooms to improve those algorithms, and one of the way is to alter the formula of an algorithm. In this study, we define algorithm designing as an optimization problem, and use genetic programming to find new mutation schemes for differential evolution. In addition, we evaluate the generated mutation schemes using several benchmarks to verify that the proposed methods and the generated algorithms are effective ones.
KW - Differential evolution
KW - Evolutionary algorithm designing
KW - Genetic programming
UR - http://www.scopus.com/inward/record.url?scp=84888586419&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84888586419&partnerID=8YFLogxK
M3 - Conference contribution
SP - 1548
EP - 1549
BT - Proceedings of the SICE Annual Conference
ER -