Mining Potentially Explanatory Patterns via Partial Solutions
Authors: 
Giancarlo Antonino Pasquale Ignazio Catalano, Alexander E. I Brownlee, David Cairns, John Mccall, Russell AinslieAuthors Info & Claims
Giancarlo Antonino Pasquale Ignazio Catalano, Alexander E. I Brownlee, David Cairns, John Mccall, Russell AinslieAuthors Info & ClaimsPages 567 - 570
Abstract
We introduce Partial Solutions to improve the explainability of genetic algorithms for combinatorial optimization. Partial Solutions represent beneficial traits found by analyzing a population, and are presented to the user for explainability, but also provide an explicit model from which new solutions can be generated. We present an algorithm that assembles a collection of explanatory Partial Solutions chosen to strike a balance between simplicity, high fitness and atomicity, that are shown to be able to solve standard optimization benchmarks.
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References
[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]
[6]
Michael R. Garey and David S. Johnson. 2006. Computers and Intractability: A Guide to the Theory of NP-Completeness. 24 (7 2006), 90--91. Issue 1.
[7]
David E Goldberg. 1989. Genetic algorithms and Walsh functions: Part 2, Deception and its analysis. Complex systems 3 (1989), 153--171.
[8]
David E. Goldberg, Kumara Sastry, and Yukio Ohsawa. 2003. Discovering Deep Building Blocks for Competent Genetic Algorithms Using Chance Discovery via KeyGraphs. Springer, Berlin, Heidelberg, Tokyo, Japan, 276--301.
[9]
Katja Grace, John Salvatier, Allan Dafoe, Baobao Zhang, and Owain Evans. 2018. Viewpoint: When Will AI Exceed Human Performance? Journal of Artificial Intelligence Research 62 (7 2018), 729--754.
[10]
Shih-Huan Hsu and Tian-Li Yu. 2015. Optimization by pairwise linkage detection, incremental linkage set, and restricted/back mixing: DSMGA-II. In Proceedings of GECCO 2015. ACM, New York, NY, USA, 519--526.
[11]
Melanie Mitchell, Stephanie Forrest, and John Holland. 1992. The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance. European Conference on Artificial Life 1 (11 1992), 23--33.
[12]
L. Rabiner. 1984. Combinatorial optimization:Algorithms and complexity. IEEE Transactions on Acoustics, Speech, and Signal Processing 32 (12 1984), 1258--1259. Issue 6.
[13]
John Slaney and Toby Walsh. 2001. Backbones in Optimization and Approximation. In Proceedings of the 17th International Joint Conference on Artificial Intelligence - Volume 1 (Seattle, WA, USA) (IJCAI'01). Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 254--259.
[14]
Dirk Thierens and Peter Bosman. 2011. Optimal mixing evolutionary algorithms. In Proceedings of the 13th GECCO conference. ACM, New York, NY, USA, 617--624.
[15]
Pamela H. Vance. 2006. Knapsack Problems: Algorithms and Computer Implementations (S. Martello and P. Toth). 35 (7 2006), 684--685. Issue 4.
[16]
Feiyu Xu, Hans Uszkoreit, Yangzhou Du, Wei Fan, Dongyan Zhao, and Jun Zhu. 2019. Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges. 11839 LNAI (2019), 563--574.
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Published: 01 August 2024
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GECCO '24 Companion
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GECCO '24 Companion: Genetic and Evolutionary Computation Conference Companion
July 14 - 18, 2024
VIC, Melbourne, Australia
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