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 |
|---|---|
| Pages | 1548-1549 |
| Number of pages | 2 |
| Publication status | Published - 2013 Jan 1 |
| 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 |
Keywords
- Differential evolution
- Evolutionary algorithm designing
- Genetic programming
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications
- Electrical and Electronic Engineering
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Cite this
Kanemasa, M., & Aiyoshi, E. (2013). Designing optimal updating rule for differential evolution using genetic programming. 1548-1549. Paper presented at 2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013, Nagoya, Japan.