著者

大塚 淳

出版者

科学基礎論学会

雑誌

科学基礎論研究 (ISSN:00227668)

巻号頁・発行日

vol.46, no.2, pp.71-77, 2019 (Released:2019-05-22)

参考文献数

25

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This article aims to draw a connection between organismic evolution and machine learning as recursive optimization processes. Optimization of complex systems presupposes certain forms or designs of the input-output functions. Recent literatures in evolutionary developmental biology have discussed various design features of the genotype-phenotype mapping, including neardecomposability, generative entrenchment, standardization, plasticity, canalization, and scaffolding as means to solve complex adaptive problems through recursive evolution. I point out similar problems and/or techniques exist in the machine learning literature, and sketch some common features in these two distinct fields.