Designing optimal updating rule for differential evolution using genetic programming

Minoru Kanemasa, Eitaro Aiyoshi

研究成果: Paper

抜粋

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.

元の言語English
ページ1548-1549
ページ数2
出版物ステータスPublished - 2013 1 1
イベント2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013 - Nagoya, Japan
継続期間: 2013 9 142013 9 17

Other

Other2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013
Japan
Nagoya
期間13/9/1413/9/17

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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  • これを引用

    Kanemasa, M., & Aiyoshi, E. (2013). Designing optimal updating rule for differential evolution using genetic programming. 1548-1549. 論文発表場所 2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013, Nagoya, Japan.