Evolutionary informatics

Evolutionary informatics is a subfield of informatics addressing the practice of information processing in, and the engineering of information systems for, the study of biological evolution, as well as the study of information in evolutionary systems, natural and artificial.

Information processing in the study of biological evolution

Scientists have gathered an enormous volume of information on biological evolution, and there are problems in management of that information similar to those in bioinformatics and genomics. Indeed, bioinformatics and genomics are pertinent to the study of evolution, and utilization of information from those areas is of concern in evolutionary informatics."Main page" of the Wiki for the NESCent Evolutionary Informatics Working Group, https://www.nescent.org/wg_evoinfo/Main_Page]

In 2006, the National Evolutionary Synthesis Center (NESCent), sponsored by the National Science Foundation,National Evolutionary Synthesis Center, "About the Center," http://www.nescent.org/about/] funded the NESCent Evolutionary Informatics Working Group and conference series:

Though evolutionary biologists have developed powerful tools for inferring phylogenies, detecting selection, and so on, integrating evolutionary methodology into workflows in bioinformatics does not depend so much on the power of analysis tools as it does on a well developed informatics infrastructure: software and standards for data exchange, visualization, input-and-output, editing, control, and storage-and-retrieval. We propose a working group to facilitate (directly and indirectly) the development of this infrastructure. Through a series of four meetings, each with presentations, discussion, and actual software development, the working group will build on the foundation provided by current analysis tools and available standards.Stoltzfus, A., and Vos, R. (undated) "Evolutionary informatics: Supporting interoperability in evolutionary analysis" (project summary), https://www.nescent.org/science/awards_summary.php?id=2052]

The working group has filed its first (June 2007)Stoltzfus, A., and Vos, R. (2007) "First report to NESCent," https://www.nescent.org/wg_evoinfo/First_Report_to_NESCent] and second (December 2007)Stoltzfus, A., and Vos, R. (2007) "Second report to NESCent," https://www.nescent.org/wg_evoinfo/Second_Report_to_NESCent] reports to NESCent.

tudy 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). ["Dictionary.com Unabridged (v 1.1)", http://dictionary.reference.com/browse/informatics.] Various investigators argued in the 1940's 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).Wiener, N. (1948) "Cybernetics, or Control and Communication in the Animal and the Machine," Paris, Hermann et Cie - MIT Press, Cambridge, MA.] Wiener regarded evolution as "phylogenetic learning," or accrual of information in the genome. It should be noted that 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 reportWolpert, D.H., Macready, W.G. (1995) No Free Lunch Theorems for Search, Technical Report SFI-TR-95-02-010 (Santa Fe Institute).] and continuing with a 1997 article, "No Free Lunch Theorems for Optimization"Wolpert, D.H., Macready, W.G. (1997), "No Free Lunch Theorems for Optimization," "IEEE Transactions on Evolutionary Computation" 1, 67. http://ic.arc.nasa.gov/people/dhw/papers/78.pdf] 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,"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] and pursued the notion further in 1999.English, T.M. (1999) "Some information theoretic results on evolutionary optimization," "Proceedings of the 1999 Congress on Evolutionary Computation: CEC 99,"pp. 788-795.] 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.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] 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."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]

Wolpert and Macready proved the existence of coevolutionary "free lunches" in 2005.Wolpert, D.H., and Macready, W.G. (2005) "Coevolutionary free lunches," "IEEE Transactions on Evolutionary Computation", 9(6): 721-735] 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.

Controversy

In 2007, Professor Robert J. Marks II included among his web pages at the Baylor University website some pages regarding an "Evolutionary Informatics Laboratory." The university's administration subsequently removed those pages, which included unpublished scholarly papers coauthored by Marks and intelligent design advocate William A. Dembski. [St. Amant, C. (2007) "New intelligent design conflict hits BU," The Lariat [publication of Baylor University] , Sept. 11, 2007, http://www.baylor.edu/Lariat/news.php?action=story&story=46756] . The lab's website is now [http://www.EvolutionaryInformatics.org EvolutionaryInformatics.org] . The home page includes this definition:

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.

This may be compared to a statement by the University of Edinburgh School of Informatics:

Informatics studies the representation, processing, and communication of information in natural and engineered systems. [...] The central notion is the transformation of information - whether by computation or communication, whether by organisms or artifacts. [...] Computational systems, whether natural or engineered, are distinguished by their great complexity, as regards both their internal structure and behaviour, and their rich interaction with the environment. Informatics seeks to understand and to construct (or reconstruct) such systems, using analytic, experimental and engineering methodologies. [University of Edinburgh School of Informatics, "Vision" statement. http://www.inf.ed.ac.uk/about/vision.html.]

As of January 2008, three unpublished technical papers by Marks and Dembski are available at the Evolutionary Informatics Laboratory's website, and none refers explicitly to evolutionary informatics. [ [http://www.evolutionaryinformatics.org/Publications.html Publications] , The Evolutionary Informatics Lab]

References

External links

* [http://darwin.hunter.cuny.edu/ Evolutionary Bioinformatics Laboratory, Hunter College, CUNY]