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| {{Refimprove|date=November 2008}}
| | Man or woman who wrote the article is called Eusebio. South Carolina is your boyfriend's birth place. The most beloved hobby for him as well as the his kids is in order to fish and he's previously been doing it for quite a while. Filing has been his profession as word spread. Go to his website to search out out more: http://prometeu.net<br><br>Check out my blog ... [http://prometeu.net clash of clans cheat no survey] |
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| The term '''adaptation''' is used in [[biology]] in relation to how living beings adapt to their environments, but with two different meanings. First, the continuous adaptation of an organism to its environment, so as to maintain itself in a viable state, through sensory feedback mechanisms. Second, the development (through evolutionary steps) of an adaptation (an anatomic structure, physiological process or behavior characteristic) that increases the probability of an organism reproducing itself.{{Citation needed|date=October 2009}}
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| Generally speaking, an '''adaptive system''' is a set of interacting or interdependent entities, real or abstract, forming an integrated whole that together are able to respond to environmental changes or changes in the interacting parts. [[Feedback loops]] represent a key feature of adaptive systems, allowing the response to changes; examples of adaptive systems include: natural [[ecosystems]], individual [[organisms]], human [[communities]], human [[organizations]], and human [[families]].
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| Some artificial systems can be adaptive as well; for instance, [[robots]] employ [[control system]]s that utilize [[feedback loop]]s to sense new conditions in their environment and adapt accordingly.
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| ==The Law of Adaptation==
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| {{quote|Every adaptive system converges to a state in which all kind of stimulation ceases.<ref>José Antonio Martín H., Javier de Lope and Darío Maravall: "'''Adaptation, Anticipation and Rationality in Natural and Artificial Systems: Computational Paradigms Mimicking Nature'''" Natural Computing, December, 2009. Vol. 8(4), pp. 757-775. [http://dx.doi.org/10.1007/s11047-008-9096-6 doi]</ref>}}
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| A formal definition of the Law of Adaptation is as follows:
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| Given a system <math>S</math>, we say that a physical event <math>E</math> is a stimulus for the system <math>S</math> if and only if the probability <math>P(S \rightarrow S'|E)</math> that the system suffers a change or be perturbed (in its elements or in its processes) when the event <math>E</math> occurs is strictly greater than the prior probability that <math>S</math> suffers a change independently of <math>E</math>:
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| :<math>P(S \rightarrow S'|E)>P(S \rightarrow S') </math>
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| ''Let <math>S</math> be an arbitrary system subject to changes in time <math>t</math> and let <math>E</math> be an arbitrary event that is a stimulus for the system <math>S</math>: we say that <math>S</math> is an adaptive system if and only if when t tends to infinity <math>(t\rightarrow \infty)</math> the probability that the system <math>S</math> change its behavior <math>(S\rightarrow S')</math> in a time step <math>t_0</math> given the event <math>E</math> is equal to the probability that the system change its behavior independently of the occurrence of the event <math>E</math>. In mathematical terms:''
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| #- <math> P_{t_0}(S\rightarrow S'|E) > P_{t_0}(S\rightarrow S') > 0 </math>
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| #- <math> \lim_{t\rightarrow \infty} P_t(S\rightarrow S' | E) = P_t(S\rightarrow S')</math>
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| Thus, for each instant <math>t</math> will exist a temporal interval <math>h</math> such that:
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| :<math> P_{t+h}(S\rightarrow S' | E) - P_{t+h}(S\rightarrow S') < P_t(S\rightarrow S' | E) - P_t(S\rightarrow S')</math>
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| == Benefit of Self-Adjusting Systems ==
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| In an adaptive system, a parameter changes slowly and has no preferred value. In a self-adjusting system though, the parameter value “depends on the history of the system dynamics”. One of the most important qualities of self-adjusting systems is its “adaption to the edge of chaos” or ability to avoid chaos. Practically speaking, by heading to the edge of chaos without going further, a leader may act spontaneously yet without disaster. A March/April 2009 Complexity article further explains the self-adjusting systems used and the realistic implications.<ref>Hübler, A. & Wotherspoon, T.: "'''Self-Adjusting Systems Avoid Chaos'''". Complexity. 14(4), 8 – 11. 2008</ref>
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| ==See also==
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| {{Portal|Evolutionary biology}}
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| * [[Adaptive immune system]]
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| * [[Artificial neural network]]
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| * [[Complex adaptive system]]
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| * [[Diffusion of innovations]]
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| * [[Ecosystems]]
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| * [[Gene expression programming]]
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| * [[Genetic algorithms]]
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| * [[Neural adaptation]]
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| ==References==
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| {{Reflist}}
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| <!--
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| * {{cite journal
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| | last = Martin H. | first = Jose Antonio. | authorlink = Jose Antonio Martin H.
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| | coauthors = [[Javier de Lope]]; [[Darío Maravall]]
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| | title = Adaptation, Anticipation and Rationality in Natural and Artificial Systems: Computational Paradigms Mimicking Nature
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| | journal = Natural Computing
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| | volume = 8(4)
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| | pages = 757-775
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| | publisher = Springer
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| | date = 2009
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| | doi = 10.1007/s11047-008-9096-6
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| }}
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| -->
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| [[Category:Control engineering]]
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| [[Category:Cybernetics]]
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| [[Category:Systems theory]]
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