Mining Potentially Explanatory Patterns via Partial Solutions-Reference-Cited by-同舟云学术

Mining Potentially Explanatory Patterns via Partial Solutions

Published:2024-07-14 Issue: Volume: Page:567-570
ISSN:
Container-title:Proceedings of the Genetic and Evolutionary Computation Conference Companion
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Author:

Catalano Giancarlo Antonino Pasquale Ignazio¹^ORCID,Brownlee Alexander E. I¹^ORCID,Cairns David¹^ORCID,Mccall John²³^ORCID,Ainslie Russell⁴^ORCID

Affiliation:

1. Computing and Mathematics, University of Stirling, Stirling, Stirlingshire, United Kingdom

2. National Subsea Centre, Aberdeen, United Kingdom

3. Robert Gordon University, Aberdeen, United Kingdom

4. Applied Research, BT Technology, Ipswich, United Kingdom

Funder

The Data Lab

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3638530.3654318

Reference16 articles.

1. Shumeet Baluja and Scott Davies. 1997. Using Optimal Dependency-Trees for Combinational Optimization. In Proceedings of the Fourteenth ICML. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 30--38.

2. Giancarlo Catalano. 2024. PS Assisted Explainability. https://github.com/Giancarlo-Catalano/PS_Minimal_Showcase

3. GianCarlo A.P.I Catalano Alexander E. I. Brownlee David Cairns John McCall and Russell Ainslie. 2024. Mining Potentially Explanatory Patterns via Partial Solutions. arXiv:2404.04388

4. Kalyanmoy Deb and Aravind Srinivasan. 2008. Innovization: Discovery of innovative design principles through multiobjective evolutionary optimization. Multiobjective Problem Solving from Nature (2008), 243--262.

5. Jack McKay Fletcher and Thomas Wennekers. 2017. A natural approach to studying schema processing. arXiv:1705.04536 [cs.NE]

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1. Mining Potentially Explanatory Patterns via Partial Solutions;Proceedings of the Genetic and Evolutionary Computation Conference Companion;2024-07-14