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Titlebook: Genetic Programming Theory and Practice XX; Stephan Winkler,Leonardo Trujillo,Ting Hu Book 2024 The Editor(s) (if applicable) and The Auth

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发表于 2025-3-21 19:37:22 | 显示全部楼层 |阅读模式
书目名称Genetic Programming Theory and Practice XX
编辑Stephan Winkler,Leonardo Trujillo,Ting Hu
视频video
概述Explores the intersection of GP and evolutionary computation, with machine learning and deep learning methods.Provides a unique combination of theoretical contributions and state-of-the-art real-world
丛书名称Genetic and Evolutionary Computation
图书封面Titlebook: Genetic Programming Theory and Practice XX;  Stephan Winkler,Leonardo Trujillo,Ting Hu Book 2024 The Editor(s) (if applicable) and The Auth
描述.Genetic Programming Theory and Practice brings together some of the most impactful researchers in the field of Genetic Programming (GP), each one working on unique and interesting intersections of theoretical development and practical applications of this evolutionary-based machine learning paradigm. Topics of particular interest for this year’s book include powerful modeling techniques through GP-based symbolic regression, novel selection mechanisms that help guide the evolutionary process, modular approaches to GP, and applications in cybersecurity, biomedicine, and program synthesis, as well as papers by practitioner of GP that focus on usability and real-world results. In summary, readers will get a glimpse of the current state of the- art in GP research..
出版日期Book 2024
关键词Genetic Programming; Genetic Programming Applications; Model Discovery; Ethics in Computer Science; Symb
版次1
doihttps://doi.org/10.1007/978-981-99-8413-8
isbn_softcover978-981-99-8415-2
isbn_ebook978-981-99-8413-8Series ISSN 1932-0167 Series E-ISSN 1932-0175
issn_series 1932-0167
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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发表于 2025-3-21 21:09:36 | 显示全部楼层
The Principles of Political Economy,nate, and would no longer generalize if the step limit was modified slightly. This indicates that step limits can have a substantial impact on evolutionary performance, and suggests we need to revisit our notions of generalization in the context of evolutionary software synthesis .
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https://doi.org/10.1057/9780230509016al evolution training, development of interpretable personal models, derivation of classification rules, glucose prediction, hypoglycemia alert, and glucose control. By harnessing the power of evolutionary algorithms, the framework optimizes model performance and adapts to individual patient charact
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Amirhosein Khandizaji,Mary Caputiselective sweeps. Fully decentralized by nature, these methods afford new observability at scale, in particular, for distributed EC systems. Such capabilities anticipate continued growth of computational resources available to EC. Accompanying open-source software aims to expedite the application of
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A Behavioral Model of Day Treatmentof local models run on GPUs. Additionally, OpenELM includes a variety of domain implementations for easy experimentation and adaptation, including several GP domains. The hope is to help researchers easily develop new approaches and applications within the nascent and largely unexplored paradigm of
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Daydreams and the Function of Fantasycontroller that processes the local observation and produces the local action. We find that, in our scenario, multi-tree-based GP is competitive with NE and tends to produce different behaviors. We then experimentally investigate the possibility of optimizing a controller using another, pre-optimize
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https://doi.org/10.1057/9781137483942es and processes. Some notations for this new framing are provided, musings on revisiting reproducibility in the context of intelligence are discussed and some preliminary thoughts for how to pursue these ideas using genetic programming for example are offered. Let’s move together towards a common m
发表于 2025-3-23 06:32:42 | 显示全部楼层
The Impact of Step Limits on Generalization and Stability in Software Synthesis,nate, and would no longer generalize if the step limit was modified slightly. This indicates that step limits can have a substantial impact on evolutionary performance, and suggests we need to revisit our notions of generalization in the context of evolutionary software synthesis .
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