Designing optimal updating rule for differential evolution using genetic programming

Minoru Kanemasa, Eitaro Aiyoshi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationProceedings of the SICE Annual Conference
Pages1548-1549
Number of pages2
Publication statusPublished - 2013
Event2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013 - Nagoya, Japan
Duration: 2013 Sep 142013 Sep 17

Other

Other2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013
CountryJapan
CityNagoya
Period13/9/1413/9/17

Fingerprint

Genetic programming
Concretes

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

Kanemasa, M., & Aiyoshi, E. (2013). Designing optimal updating rule for differential evolution using genetic programming. In Proceedings of the SICE Annual Conference (pp. 1548-1549)

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 proceedingConference contribution

Kanemasa, M & Aiyoshi, E 2013, Designing optimal updating rule for differential evolution using genetic programming. in Proceedings of the SICE Annual Conference. pp. 1548-1549, 2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013, Nagoya, Japan, 13/9/14.
Kanemasa M, Aiyoshi E. Designing optimal updating rule for differential evolution using genetic programming. In Proceedings of the SICE Annual Conference. 2013. p. 1548-1549
Kanemasa, Minoru ; Aiyoshi, Eitaro. / Designing optimal updating rule for differential evolution using genetic programming. Proceedings of the SICE Annual Conference. 2013. pp. 1548-1549
@inproceedings{f0999289e93846a09fde78fd2a00c5f4,
title = "Designing optimal updating rule for differential evolution using genetic programming",
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.",
keywords = "Differential evolution, Evolutionary algorithm designing, Genetic programming",
author = "Minoru Kanemasa and Eitaro Aiyoshi",
year = "2013",
language = "English",
pages = "1548--1549",
booktitle = "Proceedings of the SICE Annual Conference",

}

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 -