Evolving parsimonious circuits through shapley value-based genetic programming-Reference-Cited by-同舟云学术

Evolving parsimonious circuits through shapley value-based genetic programming

Published:2022-07-09 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference Companion
language:
Short-container-title:

Author:

Shi Xinming¹,Gao Jiashi²,Minku Leandro L.³,Yao Xin¹

Affiliation:

1. University of Birmingham, UK and Southern University of Science and Technology, China

2. Southern University of Science and Technology, China

3. University of Birmingham, UK

Funder

Guangdong Provincial Key Laboratory

Shenzhen Science and Technology Program

Program for Guang- dong Introducing Innovative and Enterpreneurial Teams

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3520304.3529032

Reference9 articles.

1. Automatic design of analog electronic circuits using grammatical evolution

2. Amirata Ghorbani and James Zou . 2020. Neuron shapley: Discovering the responsible neurons. arXiv preprint arXiv:2002.09815 ( 2020 ). Amirata Ghorbani and James Zou. 2020. Neuron shapley: Discovering the responsible neurons. arXiv preprint arXiv:2002.09815 (2020).

3. John R Koza , David Andre , Martin A Keane , and Forrest H Bennett III. 1999. Genetic programming III: Darwinian invention and problem solving . Vol. 3 . Morgan Kaufmann . John R Koza, David Andre, Martin A Keane, and Forrest H Bennett III. 1999. Genetic programming III: Darwinian invention and problem solving. Vol. 3. Morgan Kaufmann.

4. Automated synthesis of analog electrical circuits by means of genetic programming

5. Analog circuit optimization system based on hybrid evolutionary algorithms

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Accelerating surrogate assisted evolutionary algorithms for expensive multi-objective optimization via explainable machine learning;Swarm and Evolutionary Computation;2024-07

2. Accelerating Surrogate Assisted Evolutionary Algorithms Via Explainable Machine Learning;2024