Human Guidance Approaches for the Genetic Improvement of Software
Pages 21 - 22
Abstract
Existing research on Genetic Improvement (GI) of source code to improve performance [10] has examined the mixed application of code synthesis and traditional GI mutation/crossover to gain higher-performing individuals that are tailored to particular deployment contexts, for examples such as hash tables or scheduling algorithms. While demonstrating successful improvements, this research presents a host of challenges [9], from search space size to fitness landscape shape, which raise questions on whether GI alone is able to present a complete solution. In this position paper we propose to augment GI processes with Human Guidance (HG) to offer a co-pilot paradigm which may overcome these challenges.
References
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Ivo Gonçalves and Sara Silva. 2013. Balancing Learning and Overfitting in Genetic Programming with Interleaved Sampling of Training Data. In Proceedings of the 16th European Conference on Genetic Programming (Vienna, Austria) (EuroGP'13). Springer-Verlag, Berlin, Heidelberg, 73--84.
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Ivo Gonçalves, Sara Silva, Joana B. Melo, and Joao Carreiras. 2012. Random Sampling Technique for Overfitting Control in Genetic Programming. 218--229.
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Kori Inkpen, Shreya Chappidi, Keri Mallari, Besmira Nushi, Divya Ramesh, Pietro Michelucci, Vani Mandava, Libuše Hannah Vepřek, and Gabrielle Quinn. 2023. Advancing Human-AI Complementarity: The Impact of User Expertise and Algorithmic Tuning on Joint Decision Making. ACM Trans. Comput.-Hum. Interact. 30, 5, Article 71 (sep 2023), 29 pages.
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Harsurinder Kaur, Husanbir Singh Pannu, and Avleen Kaur Malhi. 2019. A Systematic Review on Imbalanced Data Challenges in Machine Learning: Applications and Solutions. ACM Comput. Surv. 52, 4, Article 79 (aug 2019), 36 pages.
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Hirotaka Moriguchi and Shinichi Honiden. 2010. Sustaining Behavioral Diversity in NEAT. In Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation (Portland, Oregon, USA) (GECCO '10). Association for Computing Machinery, New York, NY, USA, 611--618.
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Andrei Paleyes, Raoul-Gabriel Urma, and Neil D. Lawrence. 2022. Challenges in Deploying Machine Learning: A Survey of Case Studies. ACM Comput. Surv. 55, 6, Article 114 (dec 2022), 29 pages.
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Penny Faulkner Rainford and Barry Porter. 2021. Open Challenges in Genetic Improvement for Emergent Software Systems. In 2021 IEEE/ACM International Workshop on Genetic Improvement (GI). 43--44.
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Penny Faulkner Rainford and Barry Porter. 2022. Using Phylogenetic Analysis to Enhance Genetic Improvement. In Proceedings of the Genetic and Evolutionary Computation Conference (Boston, Massachusetts) (GECCO '22). Association for Computing Machinery, New York, NY, USA, 849--857.
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Halit Bener Suay and Sonia Chernova. 2011. Effect of human guidance and state space size on Interactive Reinforcement Learning. In 2011 RO-MAN. 1--6.
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Published In
April 2024
40 pages
ISBN:9798400705731
DOI:10.1145/3643692
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Published: 08 August 2024
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