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FAQ: comp.ai.genetic part 3/6 (A Guide to Frequently Asked Questions)
Section - Q3: Who is concerned with EAs?

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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?
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     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.

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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?

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Last Update March 27 2014 @ 02:11 PM