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Complexity, Development, and Evolution in Morphogenetic Collective Systems
Many living and non-living complex systems can be modeled and understood as
collective systems made of heterogeneous components that self-organize and
generate nontrivial morphological structures and behaviors. This chapter
presents a brief overview of our recent effort that investigated various
aspects of such morphogenetic collective systems. We first propose a
theoretical classification scheme that distinguishes four complexity levels of
morphogenetic collective systems based on the nature of their components and
interactions. We conducted a series of computational experiments using a
self-propelled particle swarm model to investigate the effects of (1)
heterogeneity of components, (2) differentiation/re-differentiation of
components, and (3) local information sharing among components, on the
self-organization of a collective system. Results showed that (a) heterogeneity
of components had a strong impact on the system's structure and behavior, (b)
dynamic differentiation/re-differentiation of components and local information
sharing helped the system maintain spatially adjacent, coherent organization,
(c) dynamic differentiation/re-differentiation contributed to the development
of more diverse structures and behaviors, and (d) stochastic re-differentiation
of components naturally realized a self-repair capability of self-organizing
morphologies. We also explored evolutionary methods to design novel
self-organizing patterns, using interactive evolutionary computation and
spontaneous evolution within an artificial ecosystem. These self-organizing
patterns were found to be remarkably robust against dimensional changes from 2D
to 3D, although evolution worked efficiently only in 2D settings.Comment: 13 pages, 8 figures, 1 table; submitted to "Evolution, Development,
and Complexity: Multiscale Models in Complex Adaptive Systems" (Springer
Proceedings in Complexity Series