Evolutionary informatics

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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

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.

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administration subsequently took down the website for having "unapproved research," which reportedly included unpublished scholarly papers coauthored by Marks and intelligent design advocate William A. Dembski.[3] Marks moved the content removed from Baylor servers to a new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.[4]

Application

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


Informatics

Study of information processing in evolutionary systems

The notion that information processing is essential to life and to evolution predates the entry of the term informatics into the English language (1966).[5] 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).[6] Wiener regarded evolution as phylogenetic learning, or accrual of information in the genome. While cybernetics and biocybernetics address information, they place an emphasis on principles of feedback and control that informatics does not.

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[7] and continuing with a 1997 article, "No Free Lunch Theorems for Optimization"[8] 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).

English argued in 1996 that there was no free lunch due to an underlying "conservation of information,"[9] and pursued the notion further in 1999.[10] In that work, 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.[11] 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."[12]

Wolpert and Macready proved the existence of coevolutionary "free lunches" in 2005.[13] 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.

External links

Notes

This article heavily reuses content from the Wikipedia article.

References

  1. ↑ 1.0 1.1 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. ↑ 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. 
  4. ↑ 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. 
  5. ↑ Dictionary.com Unabridged (v 1.1), http://dictionary.reference.com/browse/informatics.
  6. ↑ Wiener, N. (1948) Cybernetics, or Control and Communication in the Animal and the Machine, Paris, Hermann et Cie - MIT Press, Cambridge, MA.
  7. ↑ Wolpert, D.H., Macready, W.G. (1995) No Free Lunch Theorems for Search, Technical Report SFI-TR-95-02-010 (Santa Fe Institute).
  8. ↑ 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
  9. ↑ 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
  10. ↑ English, T.M. (1999) "Some information theoretic results on evolutionary optimization," Proceedings of the 1999 Congress on Evolutionary Computation: CEC 99,pp. 788-795.
  11. ↑ 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
  12. ↑ 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
  13. ↑ Wolpert, D.H., and Macready, W.G. (2005) "Coevolutionary free lunches," IEEE Transactions on Evolutionary Computation, 9(6): 721-735
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