Research on RLGA-based Hardware Evolution Optimization Technology

Min Zhu; Sheng Yi; Chunling Yang; Rongwei Feng

doi:10.1109/ICIEA48937.2020.9248179

Research on RLGA-based Hardware Evolution Optimization Technology

Min Zhu
, Sheng Yi
, Chunling Yang
, Rongwei Feng

School of Electrical Engineering and Automation, Harbin Institute of Technology
BeiJing Orient Institute of Measurement and Test Beijing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This paper discusses, comparing with the most commonly used genetic algorithms, how to obtain RLGA via researches on reinforcement learning and improves on genetic algorithms, so as to meet our requirements for hardware evolutionary operations, under the structure of the hard core microprocessor plus FPGA and the reconfigurable evolution circuit structure based on dynamic reconfiguration technology.

Original language	English
Title of host publication	Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	188-193
Number of pages	6
ISBN (Electronic)	9781728151694
DOIs	https://doi.org/10.1109/ICIEA48937.2020.9248179
State	Published - 9 Nov 2020
Externally published	Yes
Event	15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020 - Virtual, Kristiansand, Norway Duration: 9 Nov 2020 → 13 Nov 2020

Publication series

Name	Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020

Conference

Conference	15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020
Country/Territory	Norway
City	Virtual, Kristiansand
Period	9/11/20 → 13/11/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

evolutionary algorithms
evolutionary hardware
reinforcement learning
styling

Access to Document

10.1109/ICIEA48937.2020.9248179

Cite this

Zhu, M., Yi, S., Yang, C., & Feng, R. (2020). Research on RLGA-based Hardware Evolution Optimization Technology. In Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020 (pp. 188-193). Article 9248179 (Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIEA48937.2020.9248179

Zhu, Min ; Yi, Sheng ; Yang, Chunling et al. / Research on RLGA-based Hardware Evolution Optimization Technology. Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 188-193 (Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020).

@inproceedings{901a6cda72ab4c9b97749cadeed15a46,

title = "Research on RLGA-based Hardware Evolution Optimization Technology",

abstract = "This paper discusses, comparing with the most commonly used genetic algorithms, how to obtain RLGA via researches on reinforcement learning and improves on genetic algorithms, so as to meet our requirements for hardware evolutionary operations, under the structure of the hard core microprocessor plus FPGA and the reconfigurable evolution circuit structure based on dynamic reconfiguration technology.",

keywords = "evolutionary algorithms, evolutionary hardware, reinforcement learning, styling",

author = "Min Zhu and Sheng Yi and Chunling Yang and Rongwei Feng",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020 ; Conference date: 09-11-2020 Through 13-11-2020",

year = "2020",

month = nov,

day = "9",

doi = "10.1109/ICIEA48937.2020.9248179",

language = "英语",

series = "Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "188--193",

booktitle = "Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020",

address = "美国",

}

Zhu, M, Yi, S, Yang, C & Feng, R 2020, Research on RLGA-based Hardware Evolution Optimization Technology. in Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020., 9248179, Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020, Institute of Electrical and Electronics Engineers Inc., pp. 188-193, 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020, Virtual, Kristiansand, Norway, 9/11/20. https://doi.org/10.1109/ICIEA48937.2020.9248179

Research on RLGA-based Hardware Evolution Optimization Technology. / Zhu, Min; Yi, Sheng; Yang, Chunling et al.
Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 188-193 9248179 (Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Research on RLGA-based Hardware Evolution Optimization Technology

AU - Zhu, Min

AU - Yi, Sheng

AU - Yang, Chunling

AU - Feng, Rongwei

PY - 2020/11/9

Y1 - 2020/11/9

N2 - This paper discusses, comparing with the most commonly used genetic algorithms, how to obtain RLGA via researches on reinforcement learning and improves on genetic algorithms, so as to meet our requirements for hardware evolutionary operations, under the structure of the hard core microprocessor plus FPGA and the reconfigurable evolution circuit structure based on dynamic reconfiguration technology.

AB - This paper discusses, comparing with the most commonly used genetic algorithms, how to obtain RLGA via researches on reinforcement learning and improves on genetic algorithms, so as to meet our requirements for hardware evolutionary operations, under the structure of the hard core microprocessor plus FPGA and the reconfigurable evolution circuit structure based on dynamic reconfiguration technology.

KW - evolutionary algorithms

KW - evolutionary hardware

KW - reinforcement learning

KW - styling

UR - https://www.scopus.com/pages/publications/85097521891

U2 - 10.1109/ICIEA48937.2020.9248179

DO - 10.1109/ICIEA48937.2020.9248179

M3 - 会议稿件

AN - SCOPUS:85097521891

T3 - Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020

SP - 188

EP - 193

BT - Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020

Y2 - 9 November 2020 through 13 November 2020

ER -

Zhu M, Yi S, Yang C, Feng R. Research on RLGA-based Hardware Evolution Optimization Technology. In Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 188-193. 9248179. (Proceedings of the 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020). doi: 10.1109/ICIEA48937.2020.9248179

Research on RLGA-based Hardware Evolution Optimization Technology

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this