Difference between revisions of "Evolutionary informatics"

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==History==
==History==
In 2006, the National Science Foundation-sponsored National Evolutionary Synthesis Center (NESCent) promoted the NESCent Evolutionary Informatics Working Group to "develop community cohesion on issues of standards and interoperability" of the infrastructure and tools used for "integrating evolutionary methodology into biological data analysis."<ref name="EvoInfoWG">{{cite web |url=https://www.nescent.org/wg_evoinfo/Main_Page |title=Evolutionary Informatics (EvoInfo) Working Group |publisher=National Evolutionary Synthesis Center |date=14 February 2012 |accessdate=12 January 2015}}</ref> In subsequent years, NESCent became involved in creating the Hackathons, Interoperability, Phylogenies (HIP) working group and advancing several databases, libraries, and ontologies in the field of evolutionary biology.
Evolutionary informatics has evolved out of a wide variety of scientific, mathematical, and computational endeavors, including evolutionary biology, evolutionary computation, algorithmic and evolutionary algorithmic research, and software development.<ref name="ParrEvoIn" />
 
In 2006, the National Science Foundation-sponsored National Evolutionary Synthesis Center (NESCent) promoted the NESCent Evolutionary Informatics Working Group to "develop community cohesion on issues of standards and interoperability" of the infrastructure and tools used for "integrating evolutionary methodology into biological data analysis."<ref name="EvoInfoWG">{{cite web |url=https://www.nescent.org/wg_evoinfo/Main_Page |title=Evolutionary Informatics (EvoInfo) Working Group |publisher=National Evolutionary Synthesis Center |date=14 February 2012 |accessdate=12 January 2015}}</ref> In subsequent years, NESCent became involved in creating the Hackathons, Interoperability, Phylogenies (HIP) working group and advancing several databases, libraries, and ontologies in the field of evolutionary biology.<ref name="HIP">{{cite web|url=http://www.evoio.org/wiki/HIP |title=HIP |publisher=National Evolutionary Synthesis Center |date=28 February 2014 |accessdate=13 January 2015}}</ref><ref name="NESCentInit">{{cite web |url=http://www.nescent.org/informatics/initiatives.php |title=Informatics Initiatives: Cyberinfrastructure for Evolutionary Biology  |publisher=National Evolutionary Synthesis Center |accessdate=13 January 2015}}</ref>


In 2007, Professor Robert Marks included the term "evolutionary informatics" in the title and content of his Baylor University-hosted website ''Evolutionary Informatics Laboratory'' (EIL). The university's administration subsequently took down the website for having "unapproved research," which reportedly included unpublished scholarly papers coauthored by Marks and intelligent design advocate William A. Dembski.<ref name="AmantEIL">{{cite web |url=http://www.baylor.edu/Lariat/news.php?action=story&story=46756 |archiveurl=https://web.archive.org/web/20121003011642/http://www.baylor.edu/Lariat/news.php?action=story&story=46756 |title=New intelligent design conflict hits BU |author=St. Amant, Claire |work=The Lariat |publisher=Baylor University |date=11 September 2007 |archivedate=03 October 2012 |accessdate=12 January 2015}}</ref> Marks moved the content removed from Baylor servers to a new domain. Its front page stated the following concerning evolutionary informatics:
In 2007, Professor Robert Marks included the term "evolutionary informatics" in the title and content of his Baylor University-hosted website ''Evolutionary Informatics Laboratory'' (EIL). The university's administration subsequently took down the website for having "unapproved research," which reportedly included unpublished scholarly papers coauthored by Marks and intelligent design advocate William A. Dembski.<ref name="AmantEIL">{{cite web |url=http://www.baylor.edu/Lariat/news.php?action=story&story=46756 |archiveurl=https://web.archive.org/web/20121003011642/http://www.baylor.edu/Lariat/news.php?action=story&story=46756 |title=New intelligent design conflict hits BU |author=St. Amant, Claire |work=The Lariat |publisher=Baylor University |date=11 September 2007 |archivedate=03 October 2012 |accessdate=12 January 2015}}</ref> Marks moved the content removed from Baylor servers to a new domain. Its front page stated the following concerning evolutionary informatics:


<blockquote>Evolutionary informatics merges theories of evolution and information, thereby wedding the natural, engineering, and mathematical sciences. Evolutionary informatics studies how evolving systems incorporate, transform, and export information. The Evolutionary Informatics Laboratory explores the conceptual foundations, mathematical development, and empirical application of evolutionary informatics. The principal theme of the lab’s research is teasing apart the respective roles of internally generated and externally applied information in the performance of evolutionary systems.<ref name="EILArch1">{{cite web |url=http://www.evolutionaryinformatics.org/ |archiveurl=https://web.archive.org/web/20070905131952/http://www.evolutionaryinformatics.org/ |title=The Evolutionary Informatics Lab |author=Marks, Robert |archivedate=05 September 2007 |accessdate=12 January 2015}}</ref></blockquote>
<blockquote>Evolutionary informatics merges theories of evolution and information, thereby wedding the natural, engineering, and mathematical sciences. Evolutionary informatics studies how evolving systems incorporate, transform, and export information. The Evolutionary Informatics Laboratory explores the conceptual foundations, mathematical development, and empirical application of evolutionary informatics. The principal theme of the lab’s research is teasing apart the respective roles of internally generated and externally applied information in the performance of evolutionary systems.<ref name="EILArch1">{{cite web |url=http://www.evolutionaryinformatics.org/ |archiveurl=https://web.archive.org/web/20070905131952/http://www.evolutionaryinformatics.org/ |title=The Evolutionary Informatics Lab |author=Marks, Robert |archivedate=05 September 2007 |accessdate=12 January 2015}}</ref></blockquote>
In June 2010, the first ever Informatics for Phylogenetics, Evolution, and Biodiversity (iEvoBio) Conference took place in Portland, Oregon, with the goal of "both to catalyse the development of new tools, and to increase awareness of the possibilities offered by existing technologies."<ref name="iEvoBio10">{{cite web |url=http://ievobio.org/2010/about.html |title=iEvoBio: About the Conference |publisher=National Evolutionary Synthesis Center |date=08 March 2010 |accessdate=13 January 2015}}</ref> The sixth annual conference is scheduled for May 2015.


==Application==
==Application==
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===Study of information processing in evolutionary systems===
===Information and evolutionary algorithms===
 
The notion that information processing is essential to the evolutionary process predates the entry of the term "[[informatics]]" into the English language around 1967.<ref name="MWInfo">{{cite web |url=http://www.merriam-webster.com/dictionary/informatics |title=Informatics |work=Merriam-Webster.com |publisher=Merriam-Webster, Inc |accessdate=13 January 2015}}</ref> Investigators were arguing as early as the 1940s that certain principles of information processing apply to both living and engineered systems, with much of their thinking encapsulated in Norbert Wiener's ''Cybernetics: or Control and Communication in the Animal and the Machine''.<ref name="Wiener48">{{cite book |url=http://books.google.com/books?id=NnM-uISyywAC&printsec=frontcover |title=Cybernetics: or Control and Communication in the Animal and the Machine |author=Wiener, Norbert |publisher=The MIT Press |year=1965 |isbn=026273009X |accessdate=13 January 2015}}</ref> Wiener regarded evolution as "phylogenetic learning," or accrual of information in the genome.<ref name="Wiener48" />
 
Modern work has focused on evolution as more of an optimization of fitness functions, addressing the role of information in optimization. Work by researchers David Wolpert and William G. Macready in the mid-1990s established that evolutionary algorithms have average performance no better than that of random search. They argued that superior performance could be achieved only if algorithms incorporate prior knowledge of problems, and provided an information-geometric analysis of how algorithms and problems are matched (and mismatched).<ref name="NFLTech">{{cite book |url=http://delta.cs.cinvestav.mx/~ccoello/compevol/nfl.pdf |format=PDF |title=No Free Lunch Theorems for Search, Technical Report SFI-TR-95-02-010 |author=Wolpert, David H.; Macready, William G |publisher=Santa Fe Institute |year=1995 |accessdate=13 January 2015}}</ref><ref name="NFLTO">{{cite journal |url=http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=585893 |title=No free lunch theorems for optimization |journal=IEEE Transactions on Evolutionary Computation |author=Wolpert, David H.; Macready, William G. |volume=1 |issue=1 |pages=67–82 |year=April 1997 |doi=10.1109/4235.585893 |accessdate=13 January 2015}}</ref>


The notion that information processing is essential to life and to evolution predates the entry of the term [[informatics]] into the English language (1966).<ref>''Dictionary.com Unabridged (v 1.1)'', http://dictionary.reference.com/browse/informatics.</ref> Various investigators argued in the 1940s that certain principles of information processing apply both in living and engineered systems, and much of their thinking is encapsulated in Norbert Wiener's ''Cybernetics, or Control and Communication in the Animal and the Machine'' (1948).<ref name=wiener>Wiener, N. (1948) ''Cybernetics, or Control and Communication in the Animal and the Machine,'' Paris, Hermann et Cie - MIT Press, Cambridge, MA.</ref> Wiener regarded evolution as ''phylogenetic learning,'' or accrual of information in the [[genomics|genome]]. While [[cybernetics]] and [[biocybernetics]] address information, they place an emphasis on principles of feedback and [[control system|control]] that [[informatics]] does not.
After reviewing Wolper and Macready's work on the "no free lunch" theorems, researcher Thomas M. English argued there was no free lunch due to an underlying "conservation of information," and that the duo's work "mistakes selection bias for prior information of the objective function."<ref name="EnglishOpti">{{cite journal |url=http://www.BoundedTheoretics.com/EP96.pdf |format=PDF |title=Evaluation of Evolutionary and Genetic Optimizers: No Free Lunch |journal=Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming |author=English, Thomas M.; Fogel, L. J. (Ed.); Angeline, P. J. (Ed.); Bäck, T. (Ed.) |pages=163–169 |accessdate=13 January 2015}}</ref> In 2000, English turned to Kolmogorov complexity as a measure of information in instances of fitness functions and optimization algorithms. He observed that almost all problems exhibit a high degree of Kolmogorov randomness, and thus those problems are easy for almost all optimization algorithms.<ref name="EnglishLearning">{{cite journal |url=http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=870741 |title=Optimization is easy and learning is hard in the typical function |journal=Proceedings of the 2000 Congress on Evolutionary Computation |author=English, Thomas M |volume=2 |pages=924–931 |year=2000 |doi=10.1109/CEC.2000.870741 |accessdate=13 January 2015}}</ref> English later gave a new perspective on conservation by way of characterizing approximate satisfaction of a necessary and sufficient condition for "no free lunch."<ref name="EnglishNoMore">{{cite journal |url=http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1330861 |title=No more lunch: analysis of sequential search |journal=Congress on Evolutionary Computation, 2004 |author=English, Thomas M |volume=1 |pages=227–234 |date=2004 |doi=10.1109/CEC.2004.1330861 |accessdate=13 January 2015}}</ref>


Relatively recent work has focused on evolution as optimization of fitness functions, and has addressed the role of information in optimization. Beginning with a 1995 technical report<ref name=WM95>Wolpert, D.H., Macready, W.G. (1995) No Free Lunch Theorems for Search, Technical Report SFI-TR-95-02-010 (Santa Fe Institute).</ref> and continuing with a 1997 article, "No Free Lunch Theorems for Optimization"<ref name=WM97>Wolpert, D.H., Macready, W.G. (1997), "No Free Lunch Theorems for Optimization," ''IEEE Transactions on Evolutionary Computation'' '''1''', 67. http://ti.arc.nasa.gov/m/profile/dhw/papers/78.pdf</ref> Wolpert and Macready established that evolutionary algorithms have average performance no better than that of random search. They argued that superior performance could be achieved only if algorithms incorporate prior knowledge of problems, and provided an information-geometric analysis of how algorithms and problems are matched (and mismatched).
Wolpert and Macready went on to prove the existence of coevolutionary "free lunches" in 2005.<ref name="WMCoev">{{cite journal |url=http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1545946 |title=Coevolutionary free lunches |journal=IEEE Transactions on Evolutionary Computation |author=Wolpert, David H.; Macready, William G |volume=9 |issue=6 |pages=721–735 |date=December 2005 |doi=10.1109/TEVC.2005.856205 |accessdate=13 January 2015}}</ref> This may be interpreted as the discovery of a problem class for which some coevolutionary algorithms in biology are generally better informed than others of how to solve problems.


English argued in 1996 that there was no free lunch due to an underlying "conservation of information,"<ref name=English1996>English, T. M. 1996. "Evaluation of Evolutionary and Genetic Optimizers: No Free Lunch," in L. J. Fogel, P. J. Angeline, T. Bäck (Eds.): ''Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming,'' pp. 163-169. http://www.BoundedTheoretics.com/EP96.pdf</ref> and pursued the notion further in 1999.<ref name=English99>English, T.M. (1999) "Some information theoretic results on evolutionary optimization," ''Proceedings of the 1999 Congress on Evolutionary Computation: CEC 99,''pp. 788-795.</ref> In that work, [[Conservation Science informatics|conservation]] was characterized in terms of Shannon information and mutual information. In 2000, English turned to [[Kolmogorov complexity]] as a measure of information in instances of fitness functions and optimization algorithms. He observed that almost all problems exhibit a high degree of Kolmogorov randomness, and thus are easy for almost all optimization algorithms.<ref name=English2000>English, T. M. 2000. "Optimization Is Easy and Learning Is Hard in the Typical Function," ''Proceedings of the 2000 Congress on Evolutionary Computation: CEC00'', pp. 924-931. http://www.BoundedTheoretics.com/cec2000.pdf</ref> In 2004, English gave a new perspective on conservation by way of characterizing approximate satisfaction of a necessary and sufficient condition for "no free lunch."<ref name=English2004>English, T. (2004) No More Lunch: Analysis of Sequential Search, ''Proceedings of the 2004 IEEE Congress on Evolutionary Computation'', pp. 227-234. http://BoundedTheoretics.com/CEC04.pdf</ref>
==Further reading==


Wolpert and Macready proved the existence of coevolutionary "free lunches" in 2005.<ref name=WM-coev>Wolpert, D.H., and Macready, W.G. (2005) "Coevolutionary free lunches," ''IEEE Transactions on Evolutionary Computation'', 9(6): 721-735</ref> This may be interpreted as the discovery of a problem class for which some coevolutionary algorithms are generally better informed than others of how to solve problems.
* {{cite journal |url=http://www.ncbi.nlm.nih.gov/pubmed/22154516 |title=Evolutionary informatics: Unifying knowledge about the diversity of life |journal=Trends in Ecology and Evolution |author=Parr, Cynthia S.; Guralnick, Robert; Cellinese, Nico; Page, Roderic D.M |volume=27 |issue=2 |year=February 2012 |pages=94–103 |doi=10.1016/j.tree.2011.11.001 |pmid=22154516}}


==External links==
==External links==
* [http://darwin.hunter.cuny.edu/ Evolutionary Bioinformatics Laboratory, Hunter College, CUNY]
* [https://ievobio.wordpress.com/ iEvoBio Conference]


==Notes==
==Notes==


This article heavily reuses content from [http://en.wikipedia.org/wiki/Evolutionary_informatics the Wikipedia article].
This article reuses some content from [http://en.wikipedia.org/wiki/Evolutionary_informatics the Wikipedia article].


==References==
==References==

Revision as of 20:56, 13 January 2015

Evolutionary informatics is a sub-branch of informatics that addresses the algorithmic and technological tools (like information and analytical systems) needed to better manage data from research in ecology and evolutionary biology and answer evolutionary questions.[1]

As in bioinformatics and genomics, scientists studying biological evolution have gathered an increasingly large volume of information, resulting in information management problems. Additionally, as bioinformatics and genomics are pertinent to the study of evolution, utilization of information from those areas is of concern in evolutionary informatics.

History

Evolutionary informatics has evolved out of a wide variety of scientific, mathematical, and computational endeavors, including evolutionary biology, evolutionary computation, algorithmic and evolutionary algorithmic research, and software development.[1]

In 2006, the National Science Foundation-sponsored National Evolutionary Synthesis Center (NESCent) promoted the NESCent Evolutionary Informatics Working Group to "develop community cohesion on issues of standards and interoperability" of the infrastructure and tools used for "integrating evolutionary methodology into biological data analysis."[2] In subsequent years, NESCent became involved in creating the Hackathons, Interoperability, Phylogenies (HIP) working group and advancing several databases, libraries, and ontologies in the field of evolutionary biology.[3][4]

In 2007, Professor Robert Marks included the term "evolutionary informatics" in the title and content of his Baylor University-hosted website Evolutionary Informatics Laboratory (EIL). The university's administration subsequently took down the website for having "unapproved research," which reportedly included unpublished scholarly papers coauthored by Marks and intelligent design advocate William A. Dembski.[5] Marks moved the content removed from Baylor servers to a new domain. Its front page stated the following concerning evolutionary informatics:

Evolutionary informatics merges theories of evolution and information, thereby wedding the natural, engineering, and mathematical sciences. Evolutionary informatics studies how evolving systems incorporate, transform, and export information. The Evolutionary Informatics Laboratory explores the conceptual foundations, mathematical development, and empirical application of evolutionary informatics. The principal theme of the lab’s research is teasing apart the respective roles of internally generated and externally applied information in the performance of evolutionary systems.[6]

In June 2010, the first ever Informatics for Phylogenetics, Evolution, and Biodiversity (iEvoBio) Conference took place in Portland, Oregon, with the goal of "both to catalyse the development of new tools, and to increase awareness of the possibilities offered by existing technologies."[7] The sixth annual conference is scheduled for May 2015.

Application

Evolutionary informatics can help tackle problems and tasks such as the following[1]:


Informatics

Information and evolutionary algorithms

The notion that information processing is essential to the evolutionary process predates the entry of the term "informatics" into the English language around 1967.[8] Investigators were arguing as early as the 1940s that certain principles of information processing apply to both living and engineered systems, with much of their thinking encapsulated in Norbert Wiener's Cybernetics: or Control and Communication in the Animal and the Machine.[9] Wiener regarded evolution as "phylogenetic learning," or accrual of information in the genome.[9]

Modern work has focused on evolution as more of an optimization of fitness functions, addressing the role of information in optimization. Work by researchers David Wolpert and William G. Macready in the mid-1990s established that evolutionary algorithms have average performance no better than that of random search. They argued that superior performance could be achieved only if algorithms incorporate prior knowledge of problems, and provided an information-geometric analysis of how algorithms and problems are matched (and mismatched).[10][11]

After reviewing Wolper and Macready's work on the "no free lunch" theorems, researcher Thomas M. English argued there was no free lunch due to an underlying "conservation of information," and that the duo's work "mistakes selection bias for prior information of the objective function."[12] In 2000, English turned to Kolmogorov complexity as a measure of information in instances of fitness functions and optimization algorithms. He observed that almost all problems exhibit a high degree of Kolmogorov randomness, and thus those problems are easy for almost all optimization algorithms.[13] English later gave a new perspective on conservation by way of characterizing approximate satisfaction of a necessary and sufficient condition for "no free lunch."[14]

Wolpert and Macready went on to prove the existence of coevolutionary "free lunches" in 2005.[15] This may be interpreted as the discovery of a problem class for which some coevolutionary algorithms in biology are generally better informed than others of how to solve problems.

Further reading


External links

Notes

This article reuses some content from the Wikipedia article.

References

  1. ↑ 1.0 1.1 1.2 Parr, Cynthia S.; Guralnick, Robert; Cellinese, Nico; Page, Roderic D.M (February 2012). "Evolutionary informatics: Unifying knowledge about the diversity of life". Trends in Ecology and Evolution 27 (2): 94–103. doi:10.1016/j.tree.2011.11.001. PMID 22154516. http://www.ncbi.nlm.nih.gov/pubmed/22154516. Retrieved 12 January 2015. 
  2. ↑ "Evolutionary Informatics (EvoInfo) Working Group". National Evolutionary Synthesis Center. 14 February 2012. https://www.nescent.org/wg_evoinfo/Main_Page. Retrieved 12 January 2015. 
  3. ↑ "HIP". National Evolutionary Synthesis Center. 28 February 2014. http://www.evoio.org/wiki/HIP. Retrieved 13 January 2015. 
  4. ↑ "Informatics Initiatives: Cyberinfrastructure for Evolutionary Biology". National Evolutionary Synthesis Center. http://www.nescent.org/informatics/initiatives.php. Retrieved 13 January 2015. 
  5. ↑ St. Amant, Claire (11 September 2007). "New intelligent design conflict hits BU". The Lariat. Baylor University. Archived from the original on 03 October 2012. https://web.archive.org/web/20121003011642/http://www.baylor.edu/Lariat/news.php?action=story&story=46756. Retrieved 12 January 2015. 
  6. ↑ Marks, Robert. "The Evolutionary Informatics Lab". Archived from the original on 05 September 2007. https://web.archive.org/web/20070905131952/http://www.evolutionaryinformatics.org/. Retrieved 12 January 2015. 
  7. ↑ "iEvoBio: About the Conference". National Evolutionary Synthesis Center. 8 March 2010. http://ievobio.org/2010/about.html. Retrieved 13 January 2015. 
  8. ↑ "Informatics". Merriam-Webster.com. Merriam-Webster, Inc. http://www.merriam-webster.com/dictionary/informatics. Retrieved 13 January 2015. 
  9. ↑ 9.0 9.1 Wiener, Norbert (1965). Cybernetics: or Control and Communication in the Animal and the Machine. The MIT Press. ISBN 026273009X. http://books.google.com/books?id=NnM-uISyywAC&printsec=frontcover. Retrieved 13 January 2015. 
  10. ↑ Wolpert, David H.; Macready, William G (1995) (PDF). No Free Lunch Theorems for Search, Technical Report SFI-TR-95-02-010. Santa Fe Institute. http://delta.cs.cinvestav.mx/~ccoello/compevol/nfl.pdf. Retrieved 13 January 2015. 
  11. ↑ Wolpert, David H.; Macready, William G. (April 1997). "No free lunch theorems for optimization". IEEE Transactions on Evolutionary Computation 1 (1): 67–82. doi:10.1109/4235.585893. http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=585893. Retrieved 13 January 2015. 
  12. ↑ English, Thomas M.; Fogel, L. J. (Ed.); Angeline, P. J. (Ed.); Bäck, T. (Ed.). "Evaluation of Evolutionary and Genetic Optimizers: No Free Lunch" (PDF). Evolutionary Programming V: Proceedings of the Fifth Annual Conference on Evolutionary Programming: 163–169. http://www.BoundedTheoretics.com/EP96.pdf. Retrieved 13 January 2015. 
  13. ↑ English, Thomas M (2000). "Optimization is easy and learning is hard in the typical function". Proceedings of the 2000 Congress on Evolutionary Computation 2: 924–931. doi:10.1109/CEC.2000.870741. http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=870741. Retrieved 13 January 2015. 
  14. ↑ English, Thomas M (2004). "No more lunch: analysis of sequential search". Congress on Evolutionary Computation, 2004 1: 227–234. doi:10.1109/CEC.2004.1330861. http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1330861. Retrieved 13 January 2015. 
  15. ↑ Wolpert, David H.; Macready, William G (December 2005). "Coevolutionary free lunches". IEEE Transactions on Evolutionary Computation 9 (6): 721–735. doi:10.1109/TEVC.2005.856205. http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=1545946. Retrieved 13 January 2015. 
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