Evolution strategy with competing subpopulations

Kiyotaka Izumi, M. M A Hashem, Keigo Watanabe

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

6 Citations (Scopus)

Abstract

Evolution strategies, based on the natural evolution, are the algorithms to solve the parameter optimization problems numerically. In this paper, a new Evolution Strategy (ES) is proposed to solve optimal control problems. With a view to making a balance between exploration and exploitation, a competing sub-population based arithmetical crossover technique is proposed. The effectiveness of the proposed ES is illustrated by some simulations for the push-cart (a discrete-time optimal control model) control problem.

Original languageEnglish
Title of host publicationProceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA
PublisherIEEE
Pages306-311
Number of pages6
Publication statusPublished - 1997
Externally publishedYes
EventProceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA - Monterey, CA, USA
Duration: Jul 10 1997Jul 11 1997

Other

OtherProceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA
CityMonterey, CA, USA
Period7/10/977/11/97

Fingerprint

Evolution Strategies
Parameter Optimization
Exploitation
Crossover
Optimal Control Problem
Control Problem
Optimal Control
Discrete-time
Optimization Problem
Simulation
Model

ASJC Scopus subject areas

  • Computational Mathematics

Cite this

Izumi, K., Hashem, M. M. A., & Watanabe, K. (1997). Evolution strategy with competing subpopulations. In Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA (pp. 306-311). IEEE.

Evolution strategy with competing subpopulations. / Izumi, Kiyotaka; Hashem, M. M A; Watanabe, Keigo.

Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA. IEEE, 1997. p. 306-311.

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

Izumi, K, Hashem, MMA & Watanabe, K 1997, Evolution strategy with competing subpopulations. in Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA. IEEE, pp. 306-311, Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA, Monterey, CA, USA, 7/10/97.
Izumi K, Hashem MMA, Watanabe K. Evolution strategy with competing subpopulations. In Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA. IEEE. 1997. p. 306-311
Izumi, Kiyotaka ; Hashem, M. M A ; Watanabe, Keigo. / Evolution strategy with competing subpopulations. Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA. IEEE, 1997. pp. 306-311
@inproceedings{63e1b15cd14a41529647bff83a7aaafb,
title = "Evolution strategy with competing subpopulations",
abstract = "Evolution strategies, based on the natural evolution, are the algorithms to solve the parameter optimization problems numerically. In this paper, a new Evolution Strategy (ES) is proposed to solve optimal control problems. With a view to making a balance between exploration and exploitation, a competing sub-population based arithmetical crossover technique is proposed. The effectiveness of the proposed ES is illustrated by some simulations for the push-cart (a discrete-time optimal control model) control problem.",
author = "Kiyotaka Izumi and Hashem, {M. M A} and Keigo Watanabe",
year = "1997",
language = "English",
pages = "306--311",
booktitle = "Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA",
publisher = "IEEE",

}

TY - GEN

T1 - Evolution strategy with competing subpopulations

AU - Izumi, Kiyotaka

AU - Hashem, M. M A

AU - Watanabe, Keigo

PY - 1997

Y1 - 1997

N2 - Evolution strategies, based on the natural evolution, are the algorithms to solve the parameter optimization problems numerically. In this paper, a new Evolution Strategy (ES) is proposed to solve optimal control problems. With a view to making a balance between exploration and exploitation, a competing sub-population based arithmetical crossover technique is proposed. The effectiveness of the proposed ES is illustrated by some simulations for the push-cart (a discrete-time optimal control model) control problem.

AB - Evolution strategies, based on the natural evolution, are the algorithms to solve the parameter optimization problems numerically. In this paper, a new Evolution Strategy (ES) is proposed to solve optimal control problems. With a view to making a balance between exploration and exploitation, a competing sub-population based arithmetical crossover technique is proposed. The effectiveness of the proposed ES is illustrated by some simulations for the push-cart (a discrete-time optimal control model) control problem.

UR - http://www.scopus.com/inward/record.url?scp=0030653323&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030653323&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0030653323

SP - 306

EP - 311

BT - Proceedings of IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA

PB - IEEE

ER -