Lawrence Bull
Artificial endosymbiosis
Bull, Lawrence; Pipe, A. G.; Bull, Larry; Pipe, Anthony G.; Fogarty, Terence C.
Authors
A. G. Pipe
Lawrence Bull Larry.Bull@uwe.ac.uk
School Director (Research & Enterprise) and Professor
Tony Pipe Anthony.Pipe@uwe.ac.uk
Professor
Terence C. Fogarty
Contributors
Almeida e Costa Fernando
Editor
Abstract
© Springer-Verlag Berlin Heidelberg 1995. Symbiosis is the phenomenon in which organisms of different species live together in close association, resulting in a raised level of fitness for one or more of the organisms. Endosymbiosis is the name given to symbiotic relationships in which partners are contained within a host partner. In this paper we use a simulated model of coevolution to examine endosymbiosis and its effect on the evolutionary performance of the partners involved. We are then able to suggest the conditions under which endosymbioses are more likely to occur and why; we find they emerge between organisms within a window of their respective "chaotic gas regimes" and hence that the association represents a more stable state for the partners. An endosymbiosis’ effect on its other ecological partners’ evolution is also examined. The results are used as grounds for allowing endosymbioses to emerge within artificial coevolutionary multi-agent systems.
Presentation Conference Type | Conference Paper (published) |
---|---|
Publication Date | Jan 1, 1995 |
Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Print ISSN | 0302-9743 |
Publisher | Springer Verlag |
Peer Reviewed | Not Peer Reviewed |
Volume | 929 |
Pages | 273-289 |
Series Title | Lecture Notes in Computer Science |
Series Number | 4648 |
ISBN | ; |
DOI | https://doi.org/10.1007/3-540-59496-5_305 |
Keywords | artificial intelligence, computer applications in Life Sciences, mathematical biology in general, statistics for life sciences, medicine, health sciences, neurosciences, combinatorics |
Public URL | https://uwe-repository.worktribe.com/output/1107224 |
Publisher URL | http://dx.doi.org/10.1007/3-540-59496-5_305 |
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