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around a ring. The algorithm consists in choosing the least fit species, and then replacing it and its two closest neighbors (previous and next integer) by new species, with a new random fitness. After a long run there will be a minimum required fitness, below which species don't survive. These
67:
The model dynamics repeatedly eliminates the least adapted species and mutates it and its neighbors to recreate the interaction between species. A comprehensive study of the details of this model can be found in
173:"long-run" events are referred to as avalanches, and the model proceeds through these avalanches until it reaches a state of relative stability where all species' fitness are above a certain threshold.
413:
259:
Wei1, Li; Yang, Luo; YuanFang, Wang & AiPing, Cai. "A mean-field Bak-Sneppen model with varying interaction strength". Chinese
Science Bulletin, 2011, p. 3639.
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28:-axis (from top to the bottom) the history of the population. Each discontinuity represents an evolution. The color codes the age of the species.
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292:; Kim Sneppen (1993). "Punctuated equilibrium and criticality in a simple model of evolution".
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Kim
Sneppen (1992). "Self-organized pinning and interface growth in a random medium".
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De Langhe, Rogier (2014). "A comparison of two models of scientific progress".
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The Bak–Sneppen model has been applied to the theory of scientific progress.
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that the dynamics evolves sub-diffusively, driven by a long-range memory.
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Boettcher, Stefan; Percus, Allon (2000). "Nature's way of optimizing".
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372:"A mean-field Bak-Sneppen model with varying interaction strength"
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How Nature Works: The
Science of Self-Organized Criticality
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Li Wei1; Luo Yang; Wang YuanFang & Cai AiPing (2011).
78:. A solvable version of the model has been proposed in
20:Sample of Bak–Sneppen model evolution: on the
52:record, such as the distribution of sizes of
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219:Studies in History and Philosophy of Science
412:: CS1 maint: numeric names: authors list (
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102:based on the Bak–Sneppen model, called
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156:species, which are associated with a
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446:Mathematical and theoretical biology
24:-axis the population status, on the
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168:). They are indexed by integers
48:may explain key features of the
44:. It was developed to show how
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132:10.1016/S0004-3702(00)00007-2
355:10.1103/PhysRevLett.69.3539
316:10.1103/PhysRevLett.71.4083
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191:Evolutionary biology portal
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46:self-organized criticality
399:10.1007/s11434-011-4654-1
106:, has been introduced in
377:Chinese Science Bulletin
274:. New York: Copernicus.
334:Physical Review Letters
295:Physical Review Letters
110:Artificial Intelligence
58:punctuated equilibrium
56:and the phenomenon of
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104:extremal optimization
36:is a simple model of
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436:Evolutionary biology
199:Evolutionary biology
60:. It is named after
40:between interacting
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231:2014SHPSA..46...94D
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441:Self-organization
384:(34): 3639–3642.
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225:: 94–99.
100:heuristic
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270:(1996).
247:25051877
177:See also
88:, which
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290:Bak, P.
268:Bak, P.
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160:factor
158:fitness
140:7128022
62:Per Bak
42:species
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