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Evo-devo

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This chapter explores how principles underlying natural evo-devo (evolution and development) continue to inspire the design of artificial systems from models of cell growth through to simulated three-dimensional evolved creatures. Research on biological evolvability shows that phenotypic outcomes depend on multiple interactions across different organizational levels—the adult organism is the outcome of a series of genetic cascades modulated in time and space by the wider embryological, bodily, and environmental context. This chapter reviews evo-devo principles discovered in biology and explores their potential for improving the evolvability of artificial systems. Biological topics covered include adaptive, selective, and generative mechanisms, and the role of epigenetic processes in creating phenotypic diversity. Modeling approaches include L-systems, Boolean networks, reaction-diffusion processes, genetic algorithms, and artificial embryogeny. A particular focus is on the evolution and development of the mammalian brain and the possibility of designing, using synthetic evo-devo approaches, brain-like control architectures for biomimetic robots.
Title: Evo-devo
Description:
This chapter explores how principles underlying natural evo-devo (evolution and development) continue to inspire the design of artificial systems from models of cell growth through to simulated three-dimensional evolved creatures.
Research on biological evolvability shows that phenotypic outcomes depend on multiple interactions across different organizational levels—the adult organism is the outcome of a series of genetic cascades modulated in time and space by the wider embryological, bodily, and environmental context.
This chapter reviews evo-devo principles discovered in biology and explores their potential for improving the evolvability of artificial systems.
Biological topics covered include adaptive, selective, and generative mechanisms, and the role of epigenetic processes in creating phenotypic diversity.
Modeling approaches include L-systems, Boolean networks, reaction-diffusion processes, genetic algorithms, and artificial embryogeny.
A particular focus is on the evolution and development of the mammalian brain and the possibility of designing, using synthetic evo-devo approaches, brain-like control architectures for biomimetic robots.

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