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From: David.Beasley@cf.ac.uk (David Beasley)
Subject: FAQ: comp.ai.genetic part 6/6 (A Guide to Frequently Asked Questions)
Summary: This is part 6 of a <trilogy> entitled "The Hitch-Hiker's Guide
     to Evolutionary Computation". A periodically published list of Frequently
     Asked Questions (and their answers) about Evolutionary Algorithms,
     Life and Everything. It should be read by anyone who whishes to post
     to the comp.ai.genetic newsgroup, preferably *before* posting.
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Archive-name:   ai-faq/genetic/part6
Last-Modified:  9/20/95
Issue:          3.3

TABLE OF CONTENTS OF PART 6
     Q21: What are Gray codes, and why are they used?

     Q22: What test data is available?

     Q42: What is Life all about?
     Q42b: Is there a FAQ to this group?

     Q98: Are there any patents on EAs?

     Q99: A Glossary on EAs?

----------------------------------------------------------------------

Subject: Q21: What are Gray codes, and why are they used?

     The correct spelling is "Gray"---not  "gray",  "Grey",  or  "grey"---
     since Gray codes are named after the Frank Gray  who  patented  their
     use for shaft encoders in 1953  [1].   Gray  codes  actually  have  a
     longer history, and the inquisitive reader may want to  look  up  the
     August, 1972,  issue  of  Scientific  American,  which  contains  two
     articles of interest: one on the origin  of  binary  codes  [2],  and
     another by Martin  Gardner  on  some  entertaining  aspects  of  Gray
     codes [3].  Other references containing descriptions  of  Gray  codes
     and more modern, non-GA, applications include the second  edition  of
     Numerical  Recipes  [4],  Horowitz  and  Hill  [5],  Kozen  [6],  and
     Reingold [7].

     A Gray code represents  each  number  in  the  sequence  of  integers
     {0...2^N-1} as a binary string of length N  in  an  order  such  that
     adjacent integers have Gray code representations that differ in  only
     one bit position.  Marching through the  integer  sequence  therefore
     requires flipping just one bit at a time.  Some  call  this  defining
     property of Gray codes the "adjacency property" [8].

     Example (N=3):  The binary coding of {0...7} is {000, 001, 010,  011,
     100, 101, 110, 111}, while one Gray coding is {000,  001,  011,  010,
     110, 111, 101, 100}.  In essence, a Gray code takes a binary sequence
     and shuffles  it  to  form  some  new  sequence  with  the  adjacency
     property.  There exist,  therefore,  multiple  Gray  codings  or  any
     given N.  The example shown here belongs to a  class  of  Gray  codes
     that goes by the fancy name "binary-reflected Gray codes".  These are
     the most  commonly  seen  Gray  codes,  and  one  simple  scheme  for
     generationg such a Gray code sequence says, "start with all bits zero
     and successively flip the right-most bit that produces a new string."

     Hollstien [9] investigated the use of GAs for optimizing functions of
     two variables and claimed that  a  Gray  code  representation  worked
     slightly better than the binary representation.  He attrributed  this
     difference to the adjacency property of Gray codes.   Notice  in  the
     above example that the step from three to four requires the  flipping
     of all the bits in the binary representation.  In  general,  adjacent
     integers in the binary representaion often lie many bit flips  apart.
     This  fact makes it less likely that a MUTATION operator  can  effect
     small changes for a binary-coded INDIVIDUAL.

     A Gray code representation seems to  improve  a  MUTATION  operator's
     chances of making incremental improvements, and a  close  examination
     suggests why.  In  a  binary-coded  string  of  length  N,  a  single
     mutation in the most significant  bit  (MSB)  alters  the  number  by
     2^(N-1).  In a Gray-coded string, fewer mutations lead  to  a  change
     this large.  The user of Gray codes does, however, pay  a  price  for
     this feature: those "fewer mutations" lead to  much  larger  changes.
     In the Gray code illustrated above, for example, a single mutation of
     the left-most bit changes a zero to a seven and vice-versa, while the
     largest change a single mutation can make to a corresponding  binary-
     coded INDIVIDUAL is always four.  One might still view this aspect of
     Gray codes with some favor:  most  mutations  will  make  only  small
     changes, while the occasional  mutation  that  effects  a  truly  big
     change may initiate EXPLORATION of an  entirely  new  region  in  the
     space of CHROMOSOMEs.

     The algorithm for converting between the binary-reflected  Gray  code
     described above  and  the  standard  binary  code  turns  out  to  be
     surprisingly simple to state.  First label the bits of a binary-coded
     string B[i], where larger i's represent more  significant  bits,  and
     similarly label the corresponding Gray-coded string G[i].  We convert
     one to the other as follows:  Copy the most  significant  bit.   Then
     for each smaller i  do  either  G[i] = XOR(B[i+1], B[i])---to convert
     binary to  Gray---or B[i] = XOR(B[i+1], G[i])---to  convert  Gray  to
     binary.

     One may easily implement the above algorithm in C.   Imagine  you  do
     something like

	  typedef unsigned short ALLELE;

     and then use type "allele" for each bit in your CHROMOSOME, then  the
     following two functions will convert between  binary  and  Gray  code
     representations.  You must pass them the address  of  the  high-order
     bits for each of the two strings  as  well  as  the  length  of  each
     string.  (See  the  comment  statements  for  examples.)   NB:  These
     functions assume a chromosome arranged  as  shown  in  the  following
     illustration.

     index:    C[9]                                                    C[0]
	       *-----------------------------------------------------------*
     Char C:   |  1  |  1  |  0  |  0  |  1  |  0  |  1  |  0  |  0  |  0  |
	       *-----------------------------------------------------------*
	       ^^^^^                                                 ^^^^^
	       high-order bit                                  low-order bit

 C CODE
     /* Gray <==> binary conversion routines */
     /* written by Dan T. Abell, 7 October 1993 */
     /* please send any comments or suggestions */
     /* to dabell@quark.umd.edu */

     void gray_to_binary (Cg, Cb, n)
     /* convert chromosome of length n from */
     /* Gray code to binary representation */
     allele    *Cg,*Cb;
     int  n;
     {
	  int j;

	  *Cb = *Cg;               /* copy the high-order bit */
	  for (j = 0; j < n; j++) {
	       Cb--; Cg--;         /* for the remaining bits */
	       *Cb= *(Cb+1)^*Cg;   /* do the appropriate XOR */
	  }
     }

     void binary_to_gray(Cb, Cg, n)
     /* convert chromosome of length n from */
     /* binary to Gray code representation */
     allele    *Cb, *Cg;
     int  n;
     {
	  int j;

	  *Cg = *Cb;               /* copy the high-order bit */
	  for (j = 0; j < n; j++) {
	       Cg--; Cb--;         /* for the remaining bits */
	       *Cg= *(Cb+1)^*Cb;   /* do the appropriate XOR */
	  }
     }

     References

     [1]  F.  Gray,  "Pulse  Code  Communication", U. S. Patent 2 632 058,
     March 17, 1953.

     [2] F. G. Heath, "Origins of the Binary  Code",  Scientific  American
     v.227,n.2 (August, 1972) p.76.

     [3]   Martin   Gardner,  "Mathematical  Games",  Scientific  American
     v.227,n.2 (August, 1972) p.106.

     [4] William H. Press, et al., Numerical Recipes in C, Second  Edition
     (Cambridge University Press, 1992).

     [5]  Paul  Horowitz and Winfield Hill, The Art of Electronics, Second
     Edition (Cambridge University Press, 1989).

     [6] Dexter Kozen, The Design and Analysis  of  Algorithms  (Springer-
     Verlag, New York, NY, 1992).

     [7]  Edward  M.  Reingold, et al., Combinatorial Algorithms (Prentice
     Hall, Englewood Cliffs, NJ, 1977).

     [8] David E. Goldberg, GENETIC ALGORITHMs  in  Search,  OPTIMIZATION,
     and Machine Learning (Addison-Wesley, Reading, MA, 1989).

     [9]  R.  B.  Hollstien,  Artificial  Genetic  Adaptation  in Computer
     Control Systems (PhD thesis, University of Michigan, 1971).

------------------------------

Subject: Q22: What test data is available?

 TSP DATA
     There is a TSP library (TSPLIB) available which has many  solved  and
     semi-solved TSPs and different variants. The library is maintained by
     Gerhard Reinelt <reinelt@ares.iwr.Uni-Heidelberg.de>. It is available
     from          various          FTP          sites,         including:
     softlib.cs.rice.edu:/pub/tsplib/tsblib.tar

 OTHER DATA
     Information about Operational Research test problems in  any  of  the
     areas  listed below can be obtained by emailing <o.rlibrary@ic.ac.uk>
     with the body of the email message being just the word  "info".   The
     files  in  OR-Library  are  also  available  via  anonymous  FTP from
     mscmga.ms.ic.ac.uk:/pub/  A  WWW  page  is  also  available  at  URL:
     http://mscmga.ms.ic.ac.uk/  Instructions on how to use OR-Library can
     be found in the file "paper.txt", or  in  the  article:  J.E.Beasley,
     "OR-Library:  distributing test problems by electronic mail", Journal
     of the Operational Research Society 41(11) (1990) pp1069-1072.

     File                    Problem area

     assigninfo.txt          Assignment problem
     cspinfo.txt             Crew scheduling
     deainfo.txt             Data envelopment analysis
     gapinfo.txt             Generalised assignment problem
     mipinfo.txt             Integer programming
     lpinfo.txt              Linear programming
			     Location:
     capinfo.txt                     capacitated warehouse location
     pmedinfo.txt                    p-median
     uncapinfo.txt                   uncapacitated warehouse location
     mknapinfo.txt                   Multiple knapsack problem
     qapinfo.txt                     Quadratic assignment problem
     rcspinfo.txt                    Resource constrained shortest path
			     Scheduling:
     flowshopinfo.txt                flow shop
     jobshopinfo.txt                 job shop
     openshopinfo.txt                open shop
     scpinfo.txt             Set covering
     sppinfo.txt             Set partitioning
			     Steiner:
     esteininfo.txt                  Euclidean Steiner problem
     rsteininfo.txt                  Rectilinear Steiner problem
     steininfo.txt                   Steiner problem in graphs
     tspinfo.txt             Travelling salesman problem
			     Two-dimensional cutting:
     assortinfo.txt                  assortment problem
     cgcutinfo.txt                   constrained guillotine
     ngcutinfo.txt                   constrained non-guillotine
     gcutinfo.txt                    unconstrained guillotine
			     Vehicle routing:
     areainfo.txt                    fixed areas
     fixedinfo.txt                   fixed routes
     periodinfo.txt                  period routing
     vrpinfo.txt                     single period

     ENCORE (see Q15.3) also contains  some  test  data.  See  directories
     under /etc/data/

------------------------------

Subject: Q42: What is Life all about?

     42

     References

     Adams, D. (1979) "The Hitch Hiker's Guide to the Galaxy", London: Pan
     Books.

     Adams, D. (1980) "The Restaurant at the End of the Universe", London:
     Pan Books.

     Adams,  D.  (1982)  "Life,  the Universe and Everything", London: Pan
     Books.

     Adams, D. (1984) "So long, and thanks for all the Fish", London:  Pan
     Books.

     Adams, D. (1992) "Mostly Harmless", London: Heinemann.


------------------------------

Subject: Q42b: Is there a FAQ to this group?

     Yes.

------------------------------

Subject: Q98: Are there any patents on EAs?

     Process  patents  have  been  issued  both  for  the  Bucket  Brigade
     Algorithm in CLASSIFIER SYSTEMs: U.S. patent #4,697,242: J.H. Holland
     and  A.  Burks,  "Adaptive  computing  system capable of learning and
     discovery", 1985, issued Sept  29  1987;  and  for  GP:  U.S.  patent
     #4,935,877 (to John Koza).

     This  FAQ  does  not attempt to provide legal advice. However, use of
     the Lisp code in the book [KOZA92] is freely  licensed  for  academic
     use.  Although  those  wishing  to make commercial use of any process
     should obviously consult any patent holders in question, it is pretty
     clear  that  it's  not  in  anyone's  best  interests to stifle GA/GP
     research and/or development. Commercial licenses much like those used
     for  CAD  software  can  presumably  be obtained for the use of these
     processes where necessary.

     Jarmo Alander's massive bibliography of GAs (see  Q10.8)  includes  a
     (probably)  complete  list  of  all currently know patents.  There is
     also a periodic posting on comp.ai.neural-nets by  Gregory  Aharonian
     <srctran@world.std.com>  about  patents on Artificial Neural Networks
     (ANNs).


------------------------------

Subject: Q99: A Glossary on EAs?

     1/5 SUCCESS RULE:
	  Derived by I. Rechenberg,  the  suggestion  that  when  Gaussian
	  MUTATIONs  are  applied  to real-valued vectors in searching for
	  the minimum of a function, a rule-of-thumb to attain good  rates
	  of  error  convergence  is  to  adapt  the STANDARD DEVIATION of
	  mutations to generate one superior solution out  of  every  five
	  attempts.

 A
     ADAPTIVE BEHAVIOUR:
	  "...underlying  mechanisms  that allow animals, and potentially,
	  ROBOTs to adapt and survive in uncertain environments" --- Meyer
	  & Wilson (1991), [SAB90]

     AI:  See ARTIFICIAL INTELLIGENCE.

     ALIFE:
	  See ARTIFICIAL LIFE.

     ALLELE :
	  (biol) Each GENE is able to occupy only a particular region of a
	  CHROMOSOME, it's locus. At any given locus there may  exist,  in
	  the POPULATION, alternative forms of the gene. These alternative
	  are called alleles of one another.

	  (EC) The value of a GENE.  Hence, for a  binary  representation,
	  each gene may have an ALLELE of 0 or 1.

     ARTIFICIAL INTELLIGENCE:
	  "...the  study  of  how to make computers do things at which, at
	  the moment, people are better" --- Elaine  Rich (1988)

     ARTIFICIAL LIFE:
	  Term coined by Christopher G.  Langton  for  his  1987  [ALIFEI]
	  conference.  In  the preface of the proceedings he defines ALIFE
	  as "...the study of simple computer generated hypothetical  life
	  forms, i.e.  life-as-it-could-be."

 B
     BUILDING BLOCK:
	  (EC)  A  small,  tightly clustered group of GENEs which have co-
	  evolved  in  such  a  way  that  their  introduction  into   any
	  CHROMOSOME  will  be  likely  to  give increased FITNESS to that
	  chromosome.

	  The "building block hypothesis" [GOLD89] states  that  GAs  find
	  solutions  by first finding as many BUILDING BLOCKs as possible,
	  and then combining them together to give the highest FITNESS.

 C
     CENTRAL DOGMA:
	  (biol) The dogma that nucleic acids act  as  templates  for  the
	  synthesis  of  proteins,  but never the reverse. More generally,
	  the dogma that GENEs exert an influence over the form of a body,
	  but  the  form  of  a body is never translated back into genetic
	  code: acquired characteristics are not inherited. cf LAMARCKISM.

	  (GA)  The  dogma  that  the  behaviour  of the algorithm must be
	  analysed using the SCHEMA THEOREM.

	  (life in general) The dogma that this all is useful in a way.

	  "You guys have a dogma. A certain irrational  set  of  believes.
	  Well,  here's  my  irrational  set  of  beliefs.  Something that
	  works."

	  --- Rodney A. Brooks, [LEVY92]

     CFS: See CLASSIFIER SYSTEM.

     CHROMOSOME:
	  (biol) One of the chains of DNA  found  in  cells.   CHROMOSOMEs
	  contain  GENEs,  each  encoded as a subsection of the DNA chain.
	  Chromosomes are usually present in all  cells  in  an  organism,
	  even  though  only  a minority of them will be active in any one
	  cell.

	  (EC) A datastructure which holds a `string' of task  parameters,
	  or  GENEs.   This  may  be stored, for example, as a binary bit-
	  string, or an array of integers.

     CLASSIFIER SYSTEM:
	  A system which takes a (set of) inputs, and produces a (set  of)
	  outputs  which  indicate  some classification of the inputs.  An
	  example might take inputs from sensors in a chemical plant,  and
	  classify  them  in  terms  of: 'running ok', 'needs more water',
	  'needs less water', 'emergency'. See Q1.4 for more  information.

     COMBINATORIAL OPTIMIZATION:
	  Some tasks involve combining a set of entities in a specific way
	  (e.g.  the task of building a house).  A  general  combinatorial
	  task  involves deciding (a) the specifications of those entities
	  (e.g. what size, shape, material to make the bricks  from),  and
	  (b)  the  way in which those entities are brought together (e.g.
	  the number of bricks, and  their  relative  positions).  If  the
	  resulting  combination  of  entities  can in some way be given a
	  FITNESS score, then COMBINATORIAL OPTIMIZATION is  the  task  of
	  designing  a  set  of  entities,  and  deciding how they must be
	  configured, so  as  to  give  maximum  fitness.  cf  ORDER-BASED
	  PROBLEM.

     COMMA STRATEGY:
	  Notation  originally  proposed  in  EVOLUTION STRATEGIEs, when a
	  POPULATION of "mu" PARENTs generates "lambda" OFFSPRING and  the
	  mu  parents are discarded, leving only the lambda INDIVIDUALs to
	  compete directly.  Such a process is written  as  a  (mu,lambda)
	  search.   The  process  of  only  competing  offspring then is a
	  "comma strategy." cf.  PLUS STRATEGY.
     CONVERGED:
	  A GENE is said to have CONVERGED when 95% of the CHROMOSOMEs  in
	  the  POPULATION  all  contain the same ALLELE for that gene.  In
	  some circumstances, a population can be said to  have  converged
	  when  all  genes  have  converged. (However, this is not true of
	  populations containing multiple SPECIES, for example.)

	  Most people use "convergence" fairly loosely, to  mean  "the  GA
	  has  stopped  finding new, better solutions".  Of course, if you
	  wait long  enough,  the  GA  will  *eventually*  find  a  better
	  solution  (unless  you  have  already found the global optimum).
	  What people really mean is "I'm not willing to wait for  the  GA
	  to  find  a  new,  better  solution, because I've already waited
	  longer than I wanted to and it hasn't improved in ages."

	  An interesting discussion on convergence by Michael Vose can  be
	  found      in      GA-Digest      v8n22,      available     from
	  ftp.aic.nrl.navy.mil:/pub/galist/digests/v8n22

     CONVERGENCE VELOCITY:
	  The rate of error reduction.

     COOPERATION:
	  The behavior of two or more INDIVIDUALs acting to  increase  the
	  gains of all participating individuals.

     CROSSOVER:
	  (EC)  A  REPRODUCTION  OPERATOR  which forms a new CHROMOSOME by
	  combining parts  of  each  of  two  `parent'  chromosomes.   The
	  simplest  form  is single-point CROSSOVER, in which an arbitrary
	  point in the chromosome is  picked.  All  the  information  from
	  PARENT  A  is  copied  from the start up to the crossover point,
	  then all the information  from  parent  B  is  copied  from  the
	  crossover point to the end of the chromosome. The new chromosome
	  thus gets the head of one parent's chromosome combined with  the
	  tail  of  the  other.   Variations exist which use more than one
	  crossover point, or combine information from  parents  in  other
	  ways.

	  (biol)  A   complicated  process  which typically takes place as
	  follows:  CHROMOSOMEs,  while  engaged  in  the  production   of
	  GAMETEs,  exchange  portions of genetic material.  The result is
	  that an almost infinite variety  of  gametes  may  be  produced.
	  Subsequently,   during  sexual  REPRODUCTION,  male  and  female
	  gametes (i.e. sperm and ova) fuse to produce a new DIPLOID  cell
	  with a pair of chromosomes.

	  In  [HOLLAND92]  the sentence "When sperm and ova fuse, matching
	  CHROMOSOMEs line up with one another and then cross over partway
	  along  their  length,  thus  swapping  genetic material" is thus
	  wrong, since these two activities occur in  different  parts  of
	  the  life  cycle.  [eds note:  If sexual REPRODUCTION (the  Real
	  Thing) worked like in GAs, then Holland would be right,  but  as
	  we  all  know,   it's   not   the  case.   We just encountered a
	  Freudian  slip  of  a  Grandmaster.  BTW:    even   the   German
	  translation  of   this  article  has  this  "bug", although it's
	  well-hidden by the translator.]

     CS:  See CLASSIFIER SYSTEM.

 D
     DARWINISM:
	  (biol) Theory of EVOLUTION, proposed by Darwin,  that  evolution
	  comes    about    through    random   variation   of   heritable
	  characteristics, coupled with natural SELECTION (survival of the
	  fittest).  A  physical mechanism for this, in terms of GENEs and
	  CHROMOSOMEs, was discovered many years later.  DARWINISM is  now
	  considered   by   many  biologists  to  be  old-fashioned.   The
	  talk.origins  FAQ  contains  more  details   (See   Q10.7).   cf
	  LAMARCKISM.

	  (EC) Theory which inspired all branches of EC.

     DECEPTION:
	  The  condition  where  the  combination  of good BUILDING BLOCKs
	  leads  to  reduced  FITNESS,  rather  than  increased   fitness.
	  Proposed  by [GOLD89] as a reason for the failure of GAs on many
	  tasks.

     DIPLOID:
	  (biol) This refers to a cell which contains two copies  of  each
	  CHROMOSOME.   The  copies  are  homologous i.e. they contain the
	  same GENEs in the same sequence. In  many  sexually  reproducing
	  SPECIES,  the  genes in one of the sets of chromosomes will have
	  been inherited from the father's GAMETE (sperm), while the genes
	  in  the  other  set  of chromosomes are from the mother's gamete
	  (ovum).

     DNA: (biol) Deoxyribonucleic Acid, a double stranded macromolecule of
	  helical  structure  (comparable  to  a  spiral  staircase). Both
	  single strands are linear,  unbranched  nucleic  acid  molecules
	  build  up  from  alternating  deoxyribose  (sugar) and phosphate
	  molecules. Each deoxyribose part  is  coupled  to  a  nucleotide
	  base,  which  is  responsible for establishing the connection to
	  the other strand of the DNA.  The  4  nucleotide  bases  Adenine
	  (A),  Thymine (T), Cytosine (C) and Guanine (G) are the alphabet
	  of the genetic information. The sequences of these bases in  the
	  DNA  molecule determines the building plan of any organism. [eds
	  note: suggested reading: James  D.  Watson  (1968)  "The  Double
	  Helix", London: Weidenfeld and Nicholson]

	  (literature)  Douglas  Noel  Adams, contemporary Science Fiction
	  comedy writer. Published "The Hitch-Hiker's Guide to the Galaxy"
	  when  he  was  25 years old, which made him one of the currently
	  most  successful  British  authors.   [eds  note:  interestingly
	  Watson  was  also 25 years old, when he discovered the DNA; both
	  events are probably not interconnected; you might also  want  to
	  look  at:  Neil  Gaiman's  (1987)  "DON'T  PANIC -- The Official
	  Hitch-Hiker's Guide to the Galaxy companion", and of course  get
	  your  hands  on  the  wholly  remarkable FAQ in alt.fan.douglas-
	  adams]

     DNS: (biol) Desoxyribonukleinsaeure, German for DNA.

	  (comp) The Domain Name System, a distributed database system for
	  translating    computer    names   (e.g.   lumpi.informatik.uni-
	  dortmund.de)   into   numeric   Internet,   i.e.    IP-addresses
	  (129.217.36.140)  and  vice-versa.   DNS allows you to hook into
	  the net without remembering long lists  of  numeric  references,
	  unless  your  system  administrator  has incorrectly set-up your
	  site's system.

 E
     EC:  See EVOLUTIONARY COMPUTATION.

     ELITISM:
	  ELITISM (or  an  elitist  strategy)  is  a  mechanism  which  is
	  employed  in  some EAs which ensures that the CHROMOSOMEs of the
	  most highly fit member(s) of the POPULATION are passed on to the
	  next  GENERATION  without  being  altered  by GENETIC OPERATORs.
	  Using elitism ensures that the mamimum FITNESS of the population
	  can  never  reduce  from  one  generation  to  the next. Elitism
	  usually brings about a more rapid convergence of the population.
	  In some applications elitism improves the chances of locating an
	  optimal INDIVIDUAL, while in others it reduces it.

     ENCORE:
	  The EvolutioNary Computation REpository Network.  An network  of
	  anonymous  FTP  sites  holding  all manner of interesting things
	  related to EC.  The mirror "EClair" nodes include The  Santa  Fe
	  Institute    (USA):   alife.santafe.edu:/pub/USER-AREA/EC/   The
	  Chinese University of  Hong  Kong:  ftp.cs.cuhk.hk:/pub/EC/  The
	  University        of       Warwick       (United       Kingdom):
	  ftp.dcs.warwick.ac.uk:/pub/mirrors/EC/ EUnet  Deutschland  GmbH:
	  ftp.Germany.EU.net:/pub/research/softcomp/EC/ and The California
	  Institute of Technology: ftp.krl.caltech.edu:/pub/EC/ See  Q15.3
	  for more information.

     ENVIRONMENT:
	  (biol)  That  which  surrounds  an  organism.  Can be 'physical'
	  (abiotic), or biotic.  In both, the organism  occupies  a  NICHE
	  which  influences  its  FITNESS within the total ENVIRONMENT.  A
	  biotic  environment  may  present   frequency-dependent  fitness
	  functions  within  a  POPULATION,  that  is,  the  fitness of an
	  organism's behaviour may depend upon how many  others  are  also
	  doing  it.   Over  several  GENERATIONs, biotic environments may
	  foster  co-evolution,  in  which  fitness  is  determined   with
	  SELECTION partly by other SPECIES.

     EP:  See EVOLUTIONARY PROGRAMMING.

     EPISTASIS:
	  (biol) A "masking" or "switching" effect among GENEs.  A biology
	  textbook says: "A gene is said to be epistatic when its presence
	  suppresses  the  effect  of  a gene at another locus.  Epistatic
	  genes are sometimes called inhibiting  genes  because  of  their
	  effect on other genes which are described as hypostatic."

	  (EC)  When  EC  researchers  use  the  term  EPISTASIS, they are
	  generally referring to any  kind  of  strong  interaction  among
	  GENEs, not just masking effects. A possible definition is:

	  EPISTASIS  is  the  interaction  between  different  GENEs  in a
	  CHROMOSOME.  It is the  extent  to  which  the  contribution  to
	  FITNESS of one gene depends on the values of other genes.

	  Problems  with  little  or  no  EPISTASIS  are  trivial to solve
	  (hillclimbing is sufficient). But highly epistatic problems  are
	  difficult  to  solve,  even  for GAs.  High epistasis means that
	  BUILDING BLOCKs cannot form, and there will be DECEPTION.

     ES:  See EVOLUTION STRATEGY.

     EVOLUTION:
	  That process of change which is  assured  given  a  reproductive
	  POPULATION in which there are (1) varieties of INDIVIDUALs, with
	  some varieties being (2) heritable.  See  the  talk.origins  FAQ
	  for further details (See Q10.7).

	  "Don't assume that all people who accept EVOLUTION are atheists"

	  --- Talk.origin FAQ

     EVOLUTION STRATEGIE:

     EVOLUTION STRATEGY:
	  A type of evolutionary algorithm developed in the early 1960s in
	  Germany.   It employs real-coded parameters, and in its original
	  form, it relied on  MUTATION  as  the  search  operator,  and  a
	  POPULATION  size of one. Since then it has evolved to share many
	  features  with  GENETIC   ALGORITHMs.    See   Q1.3   for   more
	  information.

     EVOLUTIONARILY STABLE STRATEGY:
	  A  strategy that does well in a POPULATION dominated by the same
	  strategy.  (cf Maynard Smith, 1974)  Or,  in  other  words,  "An
	  'ESS'  ...  is  a  strategy  such  that, if all the members of a
	  population adopt it, no mutant strategy can  invade."   (Maynard
	  Smith "Evolution and the Theory of Games", 1982).

     EVOLUTIONARY COMPUTATION:
	  Encompasses  methods of simulating EVOLUTION on a computer.  The
	  term is relatively new and represents an effort  bring  together
	  researchers  who have been working in closely related fields but
	  following  different  paradigms.   The  field  is  now  seen  as
	  including  research in GENETIC ALGORITHMs, EVOLUTION STRATEGIEs,
	  EVOLUTIONARY PROGRAMMING, ARTIFICIAL LIFE, and so forth.  For  a
	  good overview see the editorial introduction to Vol. 1, No. 1 of
	  "Evolutionary Computation" (MIT Press, 1993).  That, along  with
	  the  papers  in  the  issue,  should  give  you  a  good idea of
	  representative research.

     EVOLUTIONARY PROGRAMMING:
	  An evolutionay algorithm developed in the mid  1960s.  It  is  a
	  stochastic  OPTIMIZATION  strategy,  which is similar to GENETIC
	  ALGORITHMs, but dispenses with  both  "genomic"  representations
	  and  with  CROSSOVER  as  a REPRODUCTION OPERATOR.  See Q1.2 for
	  more information.


     EVOLUTIONARY SYSTEMS:
	  A process or system which employs the evolutionary  dynamics  of
	  REPRODUCTION, MUTATION, competition and SELECTION.  The specific
	  forms of these  processes  are  irrelevant  to  a  system  being
	  described as "evolutionary."


     EXPECTANCY:
	  Or  expected  value.   Pertaining  to a random variable X, for a
	  continuous random variable, the expected value is:
	  E(X) = INTEGRAL(-inf, inf) [X f(X) dX].
	  The discrete expectation takes a similar form using a  summation
	  instead of an integral.

     EXPLOITATION:
	  When  traversing  a SEARCH SPACE, EXPLOITATION is the process of
	  using information gathered from previously visited points in the
	  search  space  to  determine which places might be profitable to
	  visit next.  An  example  is  hillclimbing,  which  investigates
	  adjacent  points in the search space, and moves in the direction
	  giving  the  greatest   increase   in   FITNESS.    Exploitation
	  techniques are good at finding local maxima.

     EXPLORATION:
	  The  process of visiting entirely new regions of a SEARCH SPACE,
	  to  see  if  anything  promising  may  be  found  there.  Unlike
	  EXPLOITATION,  EXPLORATION  involves  leaps  into  the  unknown.
	  Problems which have many local  maxima  can  sometimes  only  be
	  solved by this sort of random search.

 F
     FAQ: Frequently Asked Questions. See definition given near the top of
	  part 1.

     FITNESS:
	  (biol) Loosely: adaptedness.  Often measured as,  and  sometimes
	  equated to, relative reproductive success.  Also proportional to
	  expected time to extinction.  "The fit are those who  fit  their
	  existing  ENVIRONMENTs  and  whose  descendants  will fit future
	  environments."  (J.  Thoday,  "A  Century  of  Darwin",   1959).
	  Accidents of history are relevant.

	  (EC)  A  value assigned to an INDIVIDUAL which reflects how well
	  the individual solves the task in hand. A "fitness function"  is
	  used  to  map  a  CHROMOSOME  to  a  FITNESS  value.  A "fitness
	  landscape" is the hypersurface obtained by applying the  fitness
	  function to every point in the SEARCH SPACE.

     FUNCTION OPTIMIZATION:
	  For  a  function  which  takes  a set of N input parameters, and
	  returns a single output value, F, FUNCTION OPTIMIZATION  is  the
	  task  of  finding  the  set(s)  of  parameters which produce the
	  maximum (or minimum) value of F. Function OPTIMIZATION is a type
	  of VALUE-BASED PROBLEM.

     FTP: File  Transfer  Protocol. A system which allows the retrieval of
	  files stored on a remote computer. Basic FTP requires a password
	  before  access  can  be gained to the remote computer. Anonymous
	  FTP  does  not  require  a  special   password:   after   giving
	  "anonymous"  as  the user name, any password will do (typically,
	  you give your email address at this point). Files  available  by
	  FTP are specified as <ftp-site-name>:<the-complete-filename> See
	  Q15.5.

     FUNCTION SET:
	  (GP) The set of operators used in GP. These functions label  the
	  internal (non-leaf) points of the parse trees that represent the
	  programs in the POPULATION.  An example FUNCTION  SET  might  be
	  {+, -, *}.

 G
     GA:  See GENETIC ALGORITHM.

     GAME THEORY:
	  A  mathematical theory originally developed for human games, and
	  generalized to human economics and  military  strategy,  and  to
	  EVOLUTION in the theory of EVOLUTIONARILY STABLE STRATEGY.  GAME
	  THEORY comes into it's own wherever the optimum  policy  is  not
	  fixed,  but  depends upon the policy which is statistically most
	  likely to be adopted by opponents.

     GAMETE:
	  (biol) Cells which carry genetic information from their  PARENTs
	  for  the  purposes  of  sexual  REPRODUCTION.   In animals, male
	  GAMETEs are called sperm, female gametes are called ova. Gametes
	  have a HAPLOID number of CHROMOSOMEs.

     GAUSSIAN DISTRIBUTION:
	  See NORMALLY DISTRIBUTED.

     GENE:
	  (EC)  A  subsection  of a CHROMOSOME which (usually) encodes the
	  value of a single parameter.
	  (biol) A unit of heredity. May be defined in different ways  for
	  different  purposes.  Molecular biologists sometimes use it in a
	  more  abstract  sense.  Following  Williams  (cf   Q10.7)   `any
	  hereditary  information  for  which  there  is  a  favorable  or
	  unfavorable SELECTION bias equal to several or  many  times  its
	  rate of endogenous change.' cf CHROMOSOME.

     GENE-POOL:
	  The  whole  set of GENEs in a breeding POPULATION.  The metaphor
	  on which the term is based  de-emphasizes  the  undeniable  fact
	  that  genes actually go about in discrete bodies, and emphasizes
	  the idea of genes flowing about the world like a liquid.

	  "Everybody out of the GENE-POOL, now!"

	  --- Author prefers to be anonymous

     GENERATION:
	  (EC) An iteration of the measurement of FITNESS and the creation
	  of a new POPULATION by means of REPRODUCTION OPERATORs.

     GENETIC ALGORITHM:
	  A  type  of  EVOLUTIONARY  COMPUTATION  devised  by John Holland
	  [HOLLAND92].   A  model  of  machine  learning   that   uses   a
	  genetic/evolutionary  metaphor.  Implementations  typically  use
	  fixed-length  character  strings  to  represent  their   genetic
	  information,  together  with  a  POPULATION of INDIVIDUALs which
	  undergo CROSSOVER and MUTATION  in  order  to  find  interesting
	  regions of the SEARCH SPACE.  See Q1.1 for more information.

     GENETIC DRIFT:
	  Changes  in  gene/allele  frequencies  in a POPULATION over many
	  GENERATIONs,  resulting  from  chance  rather  than   SELECTION.
	  Occurs  most  rapidly  in  small  populations.  Can lead to some
	  ALLELEs  becoming   `extinct',   thus   reducing   the   genetic
	  variability in the population.

     GENETIC PROGRAMMING:
	  GENETIC  ALGORITHMs applied to programs.  GENETIC PROGRAMMING is
	  more expressive than fixed-length character string  GAs,  though
	  GAs  are  likely  to  be  more  efficient  for  some  classes of
	  problems.  See Q1.5 for more information.

     GENETIC OPERATOR:
	  A search operator acting on a coding structure that is analogous
	  to a GENOTYPE of an organism (e.g. a CHROMOSOME).

     GENOTYPE:
	  The   genetic   composition  of  an  organism:  the  information
	  contained in the GENOME.

     GENOME:
	  The entire collection of GENEs (and hence CHROMOSOMEs) possessed
	  by an organism.

     GLOBAL OPTIMIZATION:
	  The  process  by  which  a  search  is made for the extremum (or
	  extrema) of a functional  which,  in  EVOLUTIONARY  COMPUTATION,
	  corresponds  to  the  FITNESS  or error function that is used to
	  assess the PERFORMANCE of any INDIVIDUAL.

     GP:  See GENETIC PROGRAMMING.

 H
     HAPLOID:
	  (biol) This refers to cell which contains a single CHROMOSOME or
	  set  of  chromosomes,  each  consisting  of a single sequence of
	  GENEs.  An example is a GAMETE.  cf DIPLOID.

	  In EC, it is usual for INDIVIDUALs to be HAPLOID.

     HARD SELECTION:
	  SELECTION acts on competing INDIVIDUALs.   When  only  the  best
	  available   individuals   are  retained  for  generating  future
	  progeny, this is termed "hard selection."   In  contrast,  "soft
	  selection"  offers  a  probabilistic  mechanism  for  maitaining
	  individuals to be PARENTs of future progeny  despite  possessing
	  relatively poorer objective values.

 I
     INDIVIDUAL:
	  A  single  member  of  a  POPULATION.   In  EC,  each INDIVIDUAL
	  contains a CHROMOSOME  (or,  more  generally,  a  GENOME)  which
	  represents a possible solution to the task being tackled, i.e. a
	  single point in the SEARCH SPACE.  Other information is  usually
	  also stored in each individual, e.g. its FITNESS.

     INVERSION:
	  (EC)  A  REORDERING  operator  which  works by selecting two cut
	  points in a CHROMOSOME, and reversing the order of all the GENEs
	  between those two points.

 L
     LAMARCKISM:
	  Theory  of  EVOLUTION  which preceded Darwin's. Lamarck believed
	  that evolution came about through the  inheritance  of  acquired
	  characteristics.  That is, the skills or physical features which
	  an INDIVIDUAL acquires during its lifetime can be passed  on  to
	  its  OFFSPRING.   Although  Lamarckian inheritance does not take
	  place in nature, the idea has been usefully applied by  some  in
	  EC.  cf DARWINISM.

     LCS: See LEARNING CLASSIFIER SYSTEM.

     LEARNING CLASSIFIER SYSTEM:
	  A  CLASSIFIER  SYSTEM which "learns" how to classify its inputs.
	  This often involves "showing" the system many examples of  input
	  patterns,  and their corresponding correct outputs. See Q1.4 for
	  more information.

 M
     MIGRATION:
	  The transfer of (the GENEs  of)  an  INDIVIDUAL  from  one  SUB-
	  POPULATION to another.

     MOBOT:
	  MOBile ROBOT.  cf ROBOT.

     MUTATION:
	  (EC)  a  REPRODUCTION  OPERATOR  which forms a new CHROMOSOME by
	  making (usually small) alterations to the values of GENEs  in  a
	  copy of a single, PARENT chromosome.

 N
     NICHE:
	  (biol)  In  natural ecosystems, there are many different ways in
	  which animals may survive (grazing, hunting, on the  ground,  in
	  trees,   etc.),   and   each  survival  strategy  is  called  an
	  "ecological niche."  SPECIES which occupy different NICHEs (e.g.
	  one eating plants, the other eating insects) may coexist side by
	  side without competition, in a stable way. But  if  two  species
	  occupying  the  same niche are brought into the same area, there
	  will be competition,  and  eventually  the  weaker  of  the  two
	  species  will  be  made  (locally)  extinct.  Hence diversity of
	  species depends on them occupying a diversity of niches  (or  on
	  geographical separation).

	  (EC)  In  EC,  we  often  want  to  maintain  diversity  in  the
	  POPULATION.  Sometimes a FITNESS function may  be  known  to  be
	  multimodal, and we want to locate all the peaks. We may consider
	  each peak in the fitness function as analogous to a  NICHE.   By
	  applying   techniques   such  as  fitness  sharing  (Goldberg  &
	  Richardson, [ICGA87]), the  population  can  be  prevented  from
	  converging  on a single peak, and instead stable SUB-POPULATIONs
	  form at each  peak.  This  is  analogous  to  different  SPECIES
	  occupying different niches. See also SPECIES, SPECIATION.

     NORMALLY DISTRIBUTED:
	  A  random  variable  is  NORMALLY  DISTRIBUTED  if  its  density
	  function is described as
	  f(x)    =    1/sqrt(2*pi*sqr(sigma))    *    exp(-0.5*(x-mu)*(x-
	  mu)/sqr(sigma))
	  where  mu  is the mean of the random variable x and sigma is the
	  STANDARD DEVIATION.

 O
     OBJECT VARIABLES:
	  Parameters that are directly involved in assessing the  relative
	  worth of an INDIVIDUAL.

     OFFSPRING:
	  An INDIVIDUAL generated by any process of REPRODUCTION.

     OPTIMIZATION:
	  The  process  of iteratively improving the solution to a problem
	  with respect to a specified objective function.

     ORDER-BASED PROBLEM:
	  A problem where the solution must be specified in  terms  of  an
	  arrangement  (e.g.  a  linear  ordering) of specific items, e.g.
	  TRAVELLING  SALESMAN  PROBLEM,  computer   process   scheduling.
	  ORDER-BASED  PROBLEMs  are a class of COMBINATORIAL OPTIMIZATION
	  problems in which  the  entities  to  be  combined  are  already
	  determined. cf VALUE-BASED PROBLEM.

     ONTOGENESIS:
	  Refers  to  a  single  organism,  and  means the life span of an
	  organism from it's birth to death.  cf PHYLOGENESIS.

 P
     PANMICTIC POPULATION:
	  (EC, biol) A  mixed  POPULATION.   A  population  in  which  any
	  INDIVIDUAL  may  be  mated  with  any  other  individual  with a
	  probability which depends only on  FITNESS.   Most  conventional
	  evolutionary algorithms have PANMICTIC POPULATIONs.

	  The  opposite  is a POPULATION divided into groups known as SUB-
	  POPULATIONs, where INDIVIDUALs may only mate with others in  the
	  same sub-population. cf SPECIATION.

     PARENT:
	  An  INDIVIDUAL  which takes part in REPRODUCTION to generate one
	  or more other individuals, known as OFFSPRING, or children.


     PERFORMANCE:
	  cf FITNESS.

     PHENOTYPE:
	  The expressed traits of an INDIVIDUAL.

     PHYLOGENESIS:
	  Refers to  a  POPULATION  of  organisms.  The  life  span  of  a
	  POPULATION  of organisms from pre-historic times until today. cf
	  ONTOGENESIS.
     PLUS STRATEGY:
	  Notation originally proposed in  EVOLUTION  STRATEGIEs,  when  a
	  POPULATION  of "mu" PARENTs generates "lambda" OFFSPRING and all
	  mu and lambda  INDIVIDUALs  compete  directly,  the  process  is
	  written  as  a (mu+lambda) search.  The process of competing all
	  parents and offspring then is  a  "plus  strategy."  cf.   COMMA
	  STRATEGY.

     POPULATION:
	  A  group of INDIVIDUALs which may interact together, for example
	  by mating, producing OFFSPRING, etc. Typical POPULATION sizes in
	  EC range from 1 (for certain EVOLUTION STRATEGIEs)
	   to   many   thousands   (for  GENETIC  PROGRAMMING).   cf  SUB-
	  POPULATION.

 R
     RECOMBINATION:
	  cf CROSSOVER.

     REORDERING:
	  (EC) A REORDERING operator  is  a  REPRODUCTION  OPERATOR  which
	  changes  the  order  of  GENEs in a CHROMOSOME, with the hope of
	  bringing related genes closer together, thereby facilitating the
	  production of BUILDING BLOCKs.  cf INVERSION.

     REPRODUCTION:
	  (biol,  EC)  The  creation  of a new INDIVIDUAL from two PARENTs
	  (sexual REPRODUCTION).  Asexual reproduction is the creation  of
	  a new individual from a single parent.

     REPRODUCTION OPERATOR:
	  (EC)  A  mechanism  which  influences  the  way in which genetic
	  information is passed on  from  PARENT(s)  to  OFFSPRING  during
	  REPRODUCTION.   REPRODUCTION  OPERATORs  fall  into  three broad
	  categories: CROSSOVER, MUTATION and REORDERING operators.

     REQUISITE VARIETY:
	  In GENETIC ALGORITHMs, when  the  POPULATION  fails  to  have  a
	  "requisite  variety" CROSSOVER will no longer be a useful search
	  operator because it will have a propensity to simply  regenerate
	  the PARENTs.

     ROBOT:
	  "The  Encyclopedia  Galactica  defines  a  ROBOT as a mechanical
	  apparatus designed to do the work of man. The marketing division
	  of  the  Sirius Cybernetics Corporation defines a robot as `Your
	  Plastic Pal Who's Fun To Be With'."

	  --- Douglas Adams (1979)

 S
     SAFIER:
	  An  EVOLUTIONARY  COMPUTATION  FTP  Repository,   now   defunct.
	  Superceeded by ENCORE.

     SCHEMA:
	  A  pattern  of  GENE  values  in a CHROMOSOME, which may include
	  `dont care' states. Thus in a  binary  chromosome,  each  SCHEMA
	  (plural  schemata)  can  be  specified  by  a string of the same
	  length as the chromosome, with each character one of {0, 1,  #}.
	  A particular chromosome is said to `contain' a particular schema
	  if it matches the schema (e.g. chromosome 01101  matches  schema
	  #1#0#).

	  The `order' of a SCHEMA is the number of non-dont-care positions
	  specified, while the `defining length' is the  distance  between
	  the  furthest  two  non-dont-care  positions.  Thus #1##0# is of
	  order 2 and defining length 3.

     SCHEMA THEOREM:
	  Theorem devised by Holland [HOLLAND92] to explain the  behaviour
	  of  GAs.   In  essence,  it  says  that a GA gives exponentially
	  increasing  reproductive  trials  to  above  average   schemata.
	  Because each CHROMOSOME contains a great many schemata, the rate
	  of SCHEMA processing in the POPULATION is very high, leading  to
	  a phenomenon known as implicit parallelism. This gives a GA with
	  a population of size N  a  speedup  by  a  factor  of  N  cubed,
	  compared to a random search.

     SEARCH SPACE:
	  If the solution to a task can be represented by a set of N real-
	  valued parameters, then the job of finding this solution can  be
	  thought  of  as  a  search  in  an  N-dimensional space. This is
	  referred to simply as the SEARCH SPACE.  More generally, if  the
	  solution  to  a  task  can be represented using a representation
	  scheme, R, then the search space is  the  set  of  all  possible
	  configurations which may be represented in R.

     SEARCH OPERATORS:
	  Processes  used  to  generate  new  INDIVIDUALs to be evaluated.
	  SEARCH OPERATORS in GENETIC ALGORITHMs are  typically  based  on
	  CROSSOVER  and  point  MUTATION.   Search operators in EVOLUTION
	  STRATEGIEs and EVOLUTIONARY PROGRAMMING  typically  follow  from
	  the  representation  of a solution and often involve Gaussian or
	  lognormal perturbations when applied to real-valued vectors.

     SELECTION:
	  The process by which some INDIVIDUALs in a POPULATION are chosen
	  for REPRODUCTION, typically on the basis of favoring individuals
	  with higher FITNESS.

     SELF-ADAPTATION:
	  The inclusion of a mechanism  not  only  to  evolve  the  OBJECT
	  VARIABLES   of   a   solution,   but  simultaneously  to  evolve
	  information on how each solution will generate new OFFSPRING.

     SIMULATION:
	  The act of modeling a natural process.

     SOFT SELECTION:
	  The mechanism which allows inferior INDIVIDUALs in a  POPULATION
	  a  non-zero  probability  of  surviving into future GENERATIONs.
	  See HARD SELECTION.

     SPECIATION:
	  (biol) The process whereby a new SPECIES comes about.  The  most
	  common cause of SPECIATION is that of geographical isolation. If
	  a SUB-POPULATION of a single species is separated geographically
	  from  the  main  POPULATION  for a sufficiently long time, their
	  GENEs will diverge  (either  due  to  differences  in  SELECTION
	  pressures  in  different  locations,  or  simply  due to GENETIC
	  DRIFT).  Eventually, genetic differences will be so  great  that
	  members of the sub-population must be considered as belonging to
	  a different (and new) species.

	  SPECIATION is very important in evolutionary biology. Small SUB-
	  POPULATIONs can evolve much more rapidly than a large POPULATION
	  (because genetic changes don't take long to become fixed in  the
	  population).  Sometimes,  this  EVOLUTION  will produce superior
	  INDIVIDUALs which can outcompete,  and  eventually  replace  the
	  SPECIES of the original, main population.

	  (EC)  Techniques analogous to geographical isolation are used in
	  a number of GAs.  Typically, the POPULATION is divided into SUB-
	  POPULATIONs,  and  INDIVIDUALs  are  only  allowed  to mate with
	  others in the same sub-population. (A small amount of  MIGRATION
	  is performed.)

	  This   produces  many  SUB-POPULATIONs  which  differ  in  their
	  characteristics, and may be referred to as different  "species".
	  This technique can be useful for finding multiple solutions to a
	  problem, or simply maintaining diversity in the SEARCH SPACE.

	  Most   biology/genetics   textbooks   contain   information   on
	  SPECIATION.   A more detailed account can be found in "Genetics,
	  Speciation and  the  Founder  Principle",  L.V.  Giddings,  K.Y.
	  Kaneshiro  and  W.W.  Anderson  (Eds.),  Oxford University Press
	  1989.

     SPECIES:
	  (biol) There is  no  universally-agreed  firm  definition  of  a
	  SPECIES.   A  species  may be roughly defined as a collection of
	  living creatures,  having  similar  characteristics,  which  can
	  breed  together  to  produce  viable  OFFSPRING similar to their
	  PARENTs.  Members of one  species  occupy  the  same  ecological
	  NICHE.   (Members  of  different species may occupy the same, or
	  different niches.)

	  (EC) In EC the definition of  "species"  is  less  clear,  since
	  generally  it is always possible for a pair INDIVIDUALs to breed
	  together.  It is probably safest to use this term  only  in  the
	  context   of   algorithms   which   employ  explicit  SPECIATION
	  mechanisms.

	  (biol) The  existence  of  different  SPECIES  allows  different
	  ecological NICHEs to be exploited. Furthermore, the existence of
	  a variety of different species itself creates new  niches,  thus
	  allowing room for further species. Thus nature bootstraps itself
	  into almost limitless complexity and diversity.

	  Conversely, the domination of one, or a small number of  SPECIES
	  reduces  the  number  of  viable  NICHEs,  leads to a decline in
	  diversity, and a reduction in  the  ability  to  cope  with  new
	  situations.

	  "Give any one species too much rope, and they'll fuck it up"

	  --- Roger Waters, "Amused to Death", 1992

     STANDARD DEVIATION:
	  A measurement for the spread of a set of data; a measurement for
	  the variation of a random variable.

     STATISTICS:
	  Descriptive measures of data; a field of mathematics  that  uses
	  probability theory to gain insight into systems' behavior.

     STEPSIZE:
	  Typically, the average distance in the appropriate space between
	  a PARENT and its OFFSPRING.

     STRATEGY VARIABLE:
	  Evolvable parameters that relate the distribution  of  OFFSPRING
	  from a PARENT.

     SUB-POPULATION:
	  A  POPULATION  may  be  sub-divided  into  groups, known as SUB-
	  POPULATIONs, where INDIVIDUALs may only mate with others in  the
	  same  group.  (This  technique  might  be  chosen  for  parallel
	  processors).  Such  sub-divisions  may  markedly  influence  the
	  evolutionary  dynamics of a population (e.g.  Wright's 'shifting
	  balance' model).  Sub-populations  may  be  defined  by  various
	  MIGRATION constraints: islands with limited arbitrary migration;
	  stepping-stones   with   migration   to   neighboring   islands;
	  isolation-by-distance  in  which each individual mates only with
	  near neighbors. cf PANMICTIC POPULATION, SPECIATION.

     SUMMERSCHOOL:
	  (USA) One of the most interesting things in the  US  educational
	  system: class work during the summer break.

 T
     TERMINAL SET:
	  (GP)  The  set  of  terminal  (leaf)  nodes  in  the parse trees
	  representing the programs in the POPULATION.  A  terminal  might
	  be a variable, such as X, a constant value, such as 42, (cf Q42)
	  or a function taking no arguments, such as (move-north).

     TRAVELLING SALESMAN PROBLEM:
	  The travelling salesperson has the task of visiting a number  of
	  clients,  located  in different cities. The problem to solve is:
	  in what order should the cities be visited in order to  minimise
	  the total distance travelled (including returning home)? This is
	  a classical example of an ORDER-BASED PROBLEM.

     TSP: See TRAVELLING SALESMAN PROBLEM.

 U
     USENET:
	  "Usenet is like a herd of performing elephants with diarrhea  --
	  massive, difficult to redirect, awe-inspiring, entertaining, and
	  a source of mind-boggling amounts of excrement  when  you  least
	  expect it."

	  --- Gene Spafford (1992)

 V
     VALUE-BASED PROBLEM:
	  A problem where the solution must be specified in terms of a set
	  of real-valued parameters.  FUNCTION OPTIMIZATION  problems  are
	  of this type.  cf SEARCH SPACE, ORDER-BASED PROBLEM.

     VECTOR OPTIMIZATION:
	  Typically,  an  OPTIMIZATION problem wherein multiple objectives
	  must be satisfied.

 Z
     ZEN NAVIGATION:
	  A methodology with tremendous propensity to get  lost  during  a
	  hike  from  A  to  B.  ZEN NAVIGATION simply consists in finding
	  something that looks as if it knew where  it  is  going  to  and
	  follow   it.    The  results  are  more  often  surprising  than
	  successful, but it's usually being worth for the sake of the few
	  occasions when it is both.

	  Sometimes  ZEN  NAVIGATION  is  referred to as "doing scientific
	  research," where A is a state of mind being considered  as  pre-
	  PhD,  and  B (usually a different) state of mind, known as post-
	  PhD. While your time spent in state C, somewhere inbetween A and
	  B, is usually referred to as "being a nobody."




ACKNOWLEDGMENTS
     Finally, credit where credit is due. I'd like to thank all the people
     who helped in assembling this  guide,  and  their  patience  with  my
     "variations  on  English  grammar".  In  the  order  I received their
     contributions, thanks to:

 Contributors,
     Lutz  Prechelt  (University  of  Karlsruhe)  the  comp.ai.neural-nets
     FAQmeister,  for  letting  me  strip  several  ideas  from "his" FAQ.
     Ritesh "peace" Bansal (CMU) for  lots  of  comments  and  references.
     David   Beasley   (University  of  Wales)  for  a  valuable  list  of
     publications (Q12), and many further additions.  David  Corne,  Peter
     Ross,   and  Hsiao-Lan  Fang  (University  of  Edinburgh)  for  their
     TIMETABLING and JSSP entries.   Mark  Kantrowitz  (CMU)  for  mocking
     about  this-and-that, and being a "mostly valuable" source concerning
     FAQ maintenance; parts of A11  have  been  stripped  from  "his"  ai-
     faq/part4  FAQ; Mark also contributed the less verbose ARCHIVE SERVER
     infos.  The texts of Q1.1, Q1.5, Q98 and  some  entries  of  Q99  are
     courtesy  by  James  Rice  (Stanford  University),  stripped from his
     genetic-programming FAQ (Q15).  Jonathan  I.  Kamens  (MIT)  provided
     infos  on  how-to-hook-into  the  USENET FAQ system.  Una Smith (Yale
     University) contributed the better parts of  the  Internet  resources
     guide   (Q15.5).    Daniel   Polani   (Gutenberg  University,  Mainz)
     "contributed"  the  Alife  II  Video  proceedings  info.   Jim  McCoy
     (University  of  Texas)  reminded  me  of the GP archive he maintains
     (Q20).  Ron Goldthwaite (UC Davis) added definitions of  Environment,
     Evolution, Fitness, and Population to the glossary, and some thoughts
     why  Biologists  should  take  note  of  EC  (Q3).   Joachim   Geidel
     (University  of  Karlsruhe)  sent a diff of the current "navy server"
     contents and the software survey, pointing to "missing links"  (Q20).
     Richard Dawkins "Glossary" section of "The extended phenotype" served
     for many new entries, too numerous to mention here (Q99).  Mark Davis
     (New   Mexico  State  University)  wrote  the  part  on  Evolutionary
     Programming (Q1.2).  Dan Abell (University of  Maryland)  contributed
     the  section on efficient greycoding (Q21).  Walter Harms (University
     of Oldenburg) commented on introductory  EC  literature.   Lieutenant
     Colonel  J.S.  Robertson (USMA, West Point), for providing a home for
     this     subversive     posting     on     their      FTP      server
     euler.math.usma.edu/pub/misc/GA  Rosie O'Neill for suggesting the PhD
     thesis entry (Q10.11).  Charlie Pearce (University of Nottingham) for
     critical  remarks  concerning  "tables";  well,  here  they  are!  J.
     Ribeiro Filho (University College London) for the pointer to the IEEE
     Computer  GA  Software  Survey;  the  PeGAsuS  description  (Q20) was
     stripped from it.  Paul Harrald for the entry on game  playing  (Q2).
     Laurence   Moran  (Uni  Toronto)  for  corrections  to  much  of  the
     biological information in Q1 and Q99.

 Reviewers,
     Robert Elliott Smith (The University of Alabama)  reviewed  the  TCGA
     infos  (Q14),  and Nici Schraudolph (UCSD) first unconsciously, later
     consciously, provided about 97% of Q20* answers.  Nicheal Lynn Cramer
     (BBN) adjusted my historic view of GP genesis.  David Fogel (ORINCON)
     commented and helped on this-and-that (where this-and-that is closely
     related  to  EP),  and provided many missing entries for the glossary
     (Q99).  Kazuhiro M. Saito (MIT) and  Mark  D.  Smucker  (Iowa  State)
     caught  my  favorite  typo(s).   Craig  W. Reynolds was the first who
     solved one of the well-hidden puzzles in the FAQ, and also added some
     valuable  stuff.   Joachim  Born  (TU  Berlin)  updated the Evolution
     Machine (EM) entry and provided the pointer to the Bionics  technical
     report  ftp  site  (Q14).   Pattie  Maes (MIT Media Lab) reviewed the
     ALIFE IV additions to the list of conferences (Q12).  Scott D. Yelich
     (Santa Fe Institute) reviewed the SFI connectivity entry (Q15).  Rick
     Riolo (MERIT) reviewed  the  CFS-C  entry  (Q20).   Davika  Seunarine
     (Acadia  Univ.)   for smoothing out this and that.  Paul Field (Queen
     Mary and Westfield  College)  for  correcting  typos,  and  providing
     insights into the blindfold pogo-sticking nomads of the Himalayas.
 and Everybody...
     Last  not  least  I'd like to thank Hans-Paul Schwefel, Thomas Baeck,
     Frank Kursawe, Guenter Rudolph for their contributions, and the  rest
     of the Systems Analysis Research Group for wholly remarkable patience
     and almost incredible unflappability during my various extravangances
     and ego-trips during my time (1990-1993) with this group.

     It was a tremendously worthwhile experience. Thanks!







       "And all our yesterdays have lighted fools; the way to dusty death;
	out, out brief candle; life's but a walking shadow; a poor player;
	  that struts and gets his hour upon the stage; and then is heared
	   no more; it is a tale; told by an idiot, full of sound an fury,
						      signifying nothing."

						  --- Shakespeare, Macbeth





EPILOGUE
			  "Natural selection is a mechanism for generating
			     an exceedingly high degree of improbability."

				  --- Sir Ronald Aylmer Fisher (1890-1962)


     This is a GREAT quotation, it sounds like something directly out of a
	turn of the century Douglas Adams: Natural selection: the original
					    "Infinite Improbability Drive"

			 --- Craig Reynolds, on reading the previous quote

     "`The Babel fish,'  said  The  Hitch  Hiker's  Guide  to  the  Galaxy
     quietly,  `is  small,  yellow and leech-like, and probably the oddest
     thing in the Universe.  It feeds on  brainwave  energy  received  not
     from  his  own  carrier  but  from  those  around  it. It absorbs all
     unconscious mental frequencies from this brainwave energy to  nourish
     itself  with.   It  then  excretes  into  the  mind  of its carrier a
     telepathic  matrix  formed  by  combining   the   conscious   thought
     frequencies  with  nerve signals picked up from the speech centers of
     the brain which has supplied them.  The practical upshot of all  this
     is  that  if  you  stick  a  Babel fish in your ear you can instantly
     understand anything said to you in any form of language.  The  speech
     patterns you actually hear decode the brainwave matrix which has been
     fed into your mind by your Babel fish.  `Now it is such  a  bizarrely
     improbable  coincidence  than anything so mindbogglingly useful could
     have evolved purely by chance that some thinkers have chosen  to  see
     it  as a final and clinching proof of the non-existence of God.  `The
     argument goes something  like  this:  ``I  refuse  to  prove  that  I
     exist,''  says  God, ``for proof denies faith, and without faith I am
     nothing.''  ``But,'' says Man, ``The Babel fish is  a  dead  giveaway
     isn't  it?  It could not have evolved by chance. It proves you exist,
     and so therefore, by your own  arguments,  you  don't.  QED.''   ``Oh
     dear,'' says God, ``I hadn't thought of that,'' and promptly vanishes
     in a puff of logic.  ``Oh, that was easy,''  says  Man,  and  for  an
     encore  goes  on to prove that black is white and gets himself killed
     on the next zebra crossing."

						  --- Douglas Adams (1979)

     "Well, people; I really wish this thingie to turn into a paper babel-
     fish  for  all  those  young ape-descended organic life forms on this
     crazy planet, who don't have any clue about what's going on  in  this
     exciting  "new"  research  field,  called  Evolutionary  Computation.
     However, this is just a start, I  need  your  help  to  increase  the
     usefulness  of  this  guide,  especially its readability for natively
     English speaking folks;  whatever  it  is:  I'd  like  to  hear  from
     you...!"

							   --- The Editor,
						  Joerg Heitkoetter (1993)

	       "Parents of young organic life forms should be warned, that
       paper babel-fishes can be harmful, if stuck too deep into the ear."

						--- Encyclopedia Galactica




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