Emergent Collective Reproduction via Evolving Neuronal Flocks
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909320702066688 |
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| author | Le, Nam H. Watson, Richard Levin, Mike Buckley, Chrys |
| author_facet | Le, Nam H. Watson, Richard Levin, Mike Buckley, Chrys |
| contents | This study facilitates the understanding of evolutionary transitions in individuality (ETIs) through a novel artificial life framework, named VitaNova, that intricately merges self-organization and natural selection to simulate the emergence of complex, reproductive groups. By dynamically modelling individual agents within an environment that challenges them with predators and spatial constraints, VitaNova elucidates the mechanisms by which simple agents evolve into cohesive units exhibiting collective reproduction. The findings underscore the synergy between self-organized behaviours and adaptive evolutionary strategies as fundamental drivers of ETIs. This approach not only contributes to a deeper understanding of higher-order biological individuality but also sets a new precedent in the empirical investigation of ETIs, challenging and extending current theoretical frameworks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_13254 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Emergent Collective Reproduction via Evolving Neuronal Flocks Le, Nam H. Watson, Richard Levin, Mike Buckley, Chrys Populations and Evolution Artificial Intelligence Neural and Evolutionary Computing This study facilitates the understanding of evolutionary transitions in individuality (ETIs) through a novel artificial life framework, named VitaNova, that intricately merges self-organization and natural selection to simulate the emergence of complex, reproductive groups. By dynamically modelling individual agents within an environment that challenges them with predators and spatial constraints, VitaNova elucidates the mechanisms by which simple agents evolve into cohesive units exhibiting collective reproduction. The findings underscore the synergy between self-organized behaviours and adaptive evolutionary strategies as fundamental drivers of ETIs. This approach not only contributes to a deeper understanding of higher-order biological individuality but also sets a new precedent in the empirical investigation of ETIs, challenging and extending current theoretical frameworks. |
| title | Emergent Collective Reproduction via Evolving Neuronal Flocks |
| topic | Populations and Evolution Artificial Intelligence Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2409.13254 |