Surrogate-Assisted Morphology Optimization by Genetic Algorithms-Reference-Cited by-同舟云学术

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Surrogate-Assisted Morphology Optimization by Genetic Algorithms

Published:2023-07-01 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Congress on Evolutionary Computation (CEC)
language:
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Author:

Jiang Jinlin¹,Chen Yongchao²,Pei Wenbin¹,Zhang Junxiang³,Hou Yaqing¹,Ge Hongwei¹,Feng Liang⁴

Affiliation:

1. School of Computer Science and Technology, Dalian University of Technology,Dalian,China

2. The Institute of Effectiveness Evaluation of Flying Vehicle,Beijing,China

3. George Washington University,Washington,DC,USA

4. College of Computer Science, Chongqing University,Chongqing,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10253662/10253666/10254185.pdf?arnumber=10254185

Reference31 articles.

1. Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning

2. Data-efficient co-adaptation of morphology and behaviour with deep reinforcement learning;luck;Conference on Robot Learning,2020

3. Learning-in-the-loop optimization: End-to-end control and co-design of soft robots through learned deep latent representations;spielberg;Advances in neural information processing systems,2019

4. Reinforcement Learning for Improving Agent Design

5. Embodied intelligence via learning and evolution

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