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

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.

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そういえば関連して、以前科学基礎論学会誌に、深層学習と生命進化を比較したエッセイを書きました。学習と進化の最適化過程の共通性について(ちなみに本ではこういう話はする予定なし)。J-Stageで公開されてます。https://t.co/rx1Q57fXvj

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