Top Document: FAQ: comp.ai.genetic part 3/6 (A Guide to Frequently Asked Questions) Previous Document: Q2: What applications of EAs are there? Next Document: Q4: How many EAs exist? Which? See reader questions & answers on this topic! - Help others by sharing your knowledge EVOLUTIONARY COMPUTATION attracts researchers and people of quite dissimilar disciplines, i.e. EC is a interdisciplinary research field: Computer scientists Want to find out about the properties of sub-symbolic information processing with EAs and about learning, i.e. adaptive systems in general. They also build the hardware necessary to enable future EAs (precursors are already beginning to emerge) to huge real world problems, i.e. the term "massively parallel computation" [HILLIS92], springs to mind. Engineers Of many kinds want to exploit the capabilities of EAs on many areas to solve their application, esp. OPTIMIZATION problems. Roboticists Want to build MOBOTs (MOBile ROBOTs, i.e. R2D2's and #5's cousins) that navigate through uncertain ENVIRONMENTs, without using built-in "maps". The MOBOTS thus have to adapt to their surroundings, and learn what they can do "move-through-door" and what they can't "move- through-wall" on their own by "trial-and-error". Cognitive scientists Might view CFS as a possible apparatus to describe models of thinking and cognitive systems. Physicists Use EC hardware, e.g. Hillis' (Thinking Machine Corp.'s) Connection Machine to model real world problems which include thousands of variables, that run "naturally" in parallel, and thus can be modelled more easily and esp. "faster" on a parallel machine, than on a serial "PC" one. Biologists Are finding EAs useful when it comes to protein folding and other such bio-computational problems (see Q2). EAs can also be used to model the behaviour of real POPULATIONs of organisms. Some biologists are hostile to modeling, but an entire community of Population Biologists arose with the 'evolutionary synthesis' of the 1930's created by the polymaths R.A. Fisher, J.B.S. Haldane, and S. Wright. Wright's SELECTION in small populations, thereby avoiding local optima) is of current interest to both biologists and ECers -- populations are naturally parallel. A good exposition of current population Biology modeling is J. Maynard Smith's text Evolutionary Genetics. Richard Dawkin's Selfish Gene and Extended Phenotype are unparalleled (sic!) prose expositions of evolutionary processes. Rob Collins' papers are excellent parallel GA models of evolutionary processes (available in [ICGA91] and by FTP from ftp.cognet.ucla.edu/pub/alife/papers/ ). As fundamental motivation, consider Fisher's comment: "No practical biologist interested in (e.g.) sexual REPRODUCTION would be led to work out the detailed consequences experienced by organisms having three or more sexes; yet what else should [s/]he do if [s/]he wishes to understand why the sexes are, in fact, always two?" (Three sexes would make for even weirder grammar, [s/]he said...) Chemists And in particular biochemists and molecular chemists, are interested in problems such as the conformational analysis of molecular clusters and related problems in molecular sciences. The application of GAs to molecular systems has opened an interesting area of research and the number of chemists involved in it increases day-by-day. Some typical research topics include: o protein folding; o conformational analysis and energy minimization; o docking algorithms for drug-design; o solvent site prediction in macromolecules; Several papers have been published in journals such as Journal of Computational Chemistry and Journal of Computer-Aided Design. Some interesting WWW sites related to the applications of GAs to chemistry (or molecular science in general) include: o http://garage.cps.msu.edu/projects/biochem/biochem.html about GAs in biochemistry (water site prediction, drug-design and protein folding); o http://www.tc.cornell.edu/Edu/SPUR/SPUR94/Main/John.html about the application of GAs to the search of conformational energy minima; o http://cmp.ameslab.gov/cmp/CMP_Theory/gsa/gen2.html By using a GA in combiation with a Tight-binding model, David Deaven and Kai- Ming Ho founded fullerene cages (including C60) starting from random coordinates. See also Q2 for applications in biocomputing. Philosophers and some other really curious people may also be interested in EC for various reasons. User Contributions:Comment about this article, ask questions, or add new information about this topic:Top Document: FAQ: comp.ai.genetic part 3/6 (A Guide to Frequently Asked Questions) Previous Document: Q2: What applications of EAs are there? Next Document: Q4: How many EAs exist? Which? Part1 - Part2 - Part3 - Part4 - Part5 - Part6 - Single Page [ Usenet FAQs | Web FAQs | Documents | RFC Index ] Send corrections/additions to the FAQ Maintainer: David.Beasley@cs.cf.ac.uk (David Beasley)
Last Update March 27 2014 @ 02:11 PM
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