- 科学基礎論研究 (ISSN:00227668)
- vol.46, no.2, pp.71-77, 2019 (Released:2019-05-22)
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.