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FAQ: part 6/6 (A Guide to Frequently Asked Questions)
Section - Q99: A Glossary on EAs?

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     A  very  good  glossary  of  genetics  terminology  can  be  found at

     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

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



     ALLELE :
	  (biol) Each GENE is able to occupy only a particular region of a
	  CHROMOSOME, its 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.

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

	  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."

	  (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

	  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.

	  (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
	  --- Rodney A. Brooks, [LEVY92]


	  (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

	  (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.

	  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.

	  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

	  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.

	  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

	  The rate of error reduction.

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

	  (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

	  (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 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.]


	  (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 was
	  combined with the selectionism of Weismann and the  genetics  of
	  Mendel   to   form   the   Neo-Darwinian  Synthesis  during  the
	  1930s-1950s by T. Dobzhansky, E. Mayr, G. Simpson, R. Fisher, S.
	  Wright, and others. cf LAMARCKISM.

	  The  FAQ contains more details (See Q10.7).  Also,
	  the "Dictionary of Darwinism and of Evolution" (Ed.  by  Patrick
	  Tort)  was published in early 1996. It contains a vast amount of
	  information  about  what  Darwinism   is   and   (perhaps   more
	  importantly)     is     not.      Further    information    from  (in  various

	  (EC) Theory which inspired all branches of EC.

	  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

	  (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
     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

     DNS: (biol) Desoxyribonukleinsaeure, German for DNA.

	  (comp) The Domain Name System, a distributed database system for
	  translating    computer    names   (e.g.   lumpi.informatik.uni-   into   numeric   Internet,   i.e.    IP-addresses
	  (  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.



	  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 minimum 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.

	  The EvolutioNary Computation REpository Network.  An  collection
	  of  FTP  servers/World  Wide  Web  sites  holding  all manner of
	  interesting  things  related  to  EC.   See   Q15.3   for   more

	  (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.


	  (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.


	  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, of which some varieties  (3)
	  differ  in FITNESS (reproductive success). (See the
	  FAQ for discussion on this (See Q10.7).)

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

	  --- FAQ


	  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

	  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).

	  A algorithm designed to perform 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
	  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.

	  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.

	  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."

	  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.

	  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.

	  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.

     FAQ: Frequently Asked Questions. See definition given before the main
	  table of contents.

	  (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.
	  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

     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

	  (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
	  {+, -, *}.


	  A mathematical theory originally developed for human games,  and
	  generalized  to  human  economics  and military strategy, and to
	  THEORY  comes  into  its  own wherever the optimum policy is not
	  fixed, but depends upon the policy which is  statistically  most
	  likely to be adopted by opponents.

	  (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.


	  (EC) A subsection of a CHROMOSOME which  (usually)  encodes  the
	  value of a single parameter.

	  (biol) The fundamental unit of inheritance, comprising a segment
	  of DNA that codes for  one  or  several  related  functions  and
	  occupies  a  fixed position (locus) on the chromosome.  However,
	  the  term  may  be  defined  in  different  ways  for  different
	  purposes.   For  a fuller story, consult a book on genetics (See

	  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

	  (EC) An iteration of the measurement of FITNESS and the creation

	  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.

	  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.

	  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.

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

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

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

	  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.


	  (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.

	  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.

	  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.

	  (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.

	  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


	  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.

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

	  MOBile ROBOT.  cf ROBOT.

	  (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.


	  (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.

	  Cocktail party definition:

	  For any pair of search algorithms, there are "as many"  problems
	  for  which  the  first  algorithm  outperforms the second as for
	  which the reverse is true. One consequence of this is that if we
	  don't  put  any  domain  knowledge  into our algorithm, it is as
	  likely to perform worse than random search as it  is  likely  to
	  perform  better.   This  is  true  for  all algorimths including

	  More detailed description:

	  The NFL work  of  Wolpert  and  Macready  is  a  framework  that
	  addresses the core aspects of search, focusing on the connection
	  between FITNESS functions and effective search  algorithms.  The
	  central  importance of this connection is demonstrated by the No
	  Free Lunch theorem which states that averaged over all problems,
	  all  search algorithms perform equally. This result implies that
	  if we are comparing a genetic algorithm to some other  algorithm
	  (e.g.,  simulated  annealing,  or  even  random  search) and the
	  genetic algorithm performs better on  some  class  of  problems,
	  then the other algorithm necessarily performs better on problems
	  outside the class. Thus it is essential to incorporate knowledge
	  of the problem into the search algorithm.

	  The  NFL  framework  also  does  the  following:  it  provides a
	  geometric interpretation of what it means for an algorithm to be
	  well  matched  to  a  problem; it provides information theoretic
	  insight into the search procedure; it investigates  time-varying
	  fitness  functions;  it  proves  that independent of the fitness
	  function,  one   cannot   (without   prior   domain   knowledge)
	  successfully  choose  between  two  algorithms  based  on  their
	  previous behavior; it provides a number of  formal  measures  of
	  how  well an algorithm performs; and it addresses the difficulty
	  of OPTIMIZATION problems from a viewpoint outside of traditional
	  computational complexity.

	  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-
	  where  mu  is the mean of the random variable x and sigma is the

	  Parameters that are directly involved in assessing the  relative
	  worth of an INDIVIDUAL.

	  An INDIVIDUAL generated by any process of REPRODUCTION.

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

	  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.
	  problems in which  the  entities  to  be  combined  are  already
	  determined. cf VALUE-BASED PROBLEM.

	  Refers  to  a  single  organism,  and  means the life span of an
	  organism from its birth to death.  cf PHYLOGENESIS.
	  (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

	  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.

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

	  cf FITNESS.

	  The expressed traits of an INDIVIDUAL.

	  Refers to  a  POPULATION  of  organisms.  The  life  span  of  a
	  population  of organisms from pre-historic times until today. cf

	  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

	  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-


	  (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.

	  (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.

	  (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.

	  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.

	  "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)

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

	  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

	  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.

	  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.

	  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.

	  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.

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

	  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.

	  The act of modeling a natural process.

	  The mechanism which allows inferior INDIVIDUALs in a  POPULATION
	  a  non-zero  probability  of  surviving into future GENERATIONs.

	  (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

	  (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
	  (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

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

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

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

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

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

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

	  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

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

	  (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).

	  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.


	  "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)

	  A problem where the solution must be specified in terms of a set
	  of real-valued parameters.  FUNCTION OPTIMIZATION  problems  are

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

	  A methodology with a tremendous propensity to get lost during  a
	  hike  from  A  to  B.  Zen Navigation simply consists of finding
	  something that looks as if it  knows  where  it  is  going,  and
	  following  it.   The  results  are  often  more  surprising than
	  successful, but its usually worth using 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 considered as  being  pre-
	  PhD,  and  B  is a (usually a different) state of mind, known as
	  post-PhD.  Your time spent in state C, somewhere inbetween A and
	  B, is usually referred to as "being a nobody."

     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:

     Lutz  Prechelt  (University  of  Karlsruhe)  the
     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 Q11  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  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  some  of  the
     biological information in  Q1  and  Q99.   Marco  Dorigo  (Uni  Libre
     Bruxelles)  gets the award for reading the guide more thoroughly than
     (including the editors). He suggested additions to Q1.4, Q2,  Q4  and
     Q22,  and pointed out various typos.  Bill Macready (SFI) for the two
     defintions of the NFL theorem in Q99.   Cedric  Notredame  (EBI)  for
     providing  information  about  applications  of  EC  in biology (Q2).
     Fabio Pichierri (The Institute of Physical and Chemical Research) for
     information  on  the  relevance of EC to chemists (Q3).  Moshe Sipper
     (Swiss Federal Institute of Technology) for details  of  applications
     in  Cellular  Automata  and  Evolvable  Hardware  (Q2).   Hugh  Sasse
     (DeMontfort University) for tracking down  missing/outdated  URLs  in
     Q1.5 and Q15.2.

     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  (Natural
     SELECTION,  Inc.)  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

 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!
     --- The Editor, Joerg Heitkoetter (1993)

			  "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 (1993), 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

				  --- 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

     "The meeting of these guys was definitely the best bang since the big

     --- Encyclopedia Galactica

 Joerg Heitkoetter,
     was  born  in  1965 in Recklinghausen, a small but beautiful 750 year
     old town at the northern rim of the Ruhrgebiet, Germany's coal mining
     and    steel   belt.    He   was   educated   at   Hittorf-Gymnasium,
     Recklinghausen,  Ruhruniversitaet  Bochum  (RUB)   and   Universitaet
     Dortmund  (UNIDO),  where  he  read theoretical medicine, psychology,
     biology, philosophy and (for whatever reason) computer sciences.

     He volunteered as a RA in the Biomathematics Research Group from 1987
     to   1989,   at   the  former  ``Max-Planck-Institute  for  Nutrition
     Physiology,'' in Dortmund (since March 1, 1993 renamed to  ``MPI  for
     Molecular  Physiology''), and spent 3 years at the ``Systems Analysis
     Research Group,'' at the Department of  Computer  Science  of  UniDO,
     where   he  wrote  a  particularly  unsuccesful  thesis  on  LEARNING
     CLASSIFIER SYSTEMs.  In 1995, after 22 semesters, he finally gave  up
     trying  to  break Chris Langton's semester record, and dropped out of
     the academic circus. Amazingly, he's the R&D and Security manager  of
     UUNET  Deutschland  GmbH,  currently  working  on various interesting
     things in parallel.  You may visit his homepage for a mostly complete
     list          at          or

     His electronic publications range from  a  voluntary  job  as  senior
     editor of the FAQ in Usenet's newsgroup, entitled The
     Hitch-Hiker's Guide to  Evolutionary  Computation,  over  many  other
     projects he helped bootstrapping, for example Howard Gutowitz' FAQ on
     Cellular Automata, available on USENET via  comp.theory.cell-automata
     ,to  about  a dozen of so-called ``multimediagrams'' written in HTML,
     the language that builds the World-Wide Web.  The  most  useful  ones
     being  ENCORE,  the  Evolutionary Computation Repository Network that
     today, after several years of weekend hacking, is  accessible  world-
     wide.  And  the latest additions: Zooland, the definite collection of
     pointers to ARTIFICIAL LIFE resources on the 'net.

     With Adam Gaffin, a former senior newspaper reporter  from  Middlesex
     News,  Boston, MA, who is now with Networks World, he edited the most
     read book on Internet, that was launched by a joined venture of Mitch
     Kapor's  Electronic  Frontier Foundation (EFF) and the Apple Computer
     Library, initially called Big Dummy's Guide to the  Internet  it  was
     later renamed to EFF's (Extended) Guide to the Internet: A round trip
     through  Global  Networks,  Life  in  Cyberspace,  and  Everything...

     Since  a  very  special  event,  he  has severe problems to take life
     seriously,  and  consequently   started   signing   everything   with
     ``-joke'',  while  developing  a  liquid  fixation on all flavours of
     whiskey. He continues to write short stories, novels and works  on  a
     diary-like  lyrics  collection  of  questionable  content, entitled A
     Pocketful of Eloquence, which recently was remaned to Heartland,  and
     published on the web as:

     He  likes  Mickey Rourke's movies (especially Rumblefish and Barfly),
     Edmund Spenser's medieval poetry, the music  of  QUEEN,  KANSAS,  and
     MARILLION, McDonald's Hamburgers, diving into the analysis of complex
     systems of any kind, (but prefers the long-legged ones) and the books
     by  Erasmus  of  Rotterdam, Robert Sheckley, Alexei Panshin, and, you
     name it, Douglas Adams.

     Due to circumstances he lead a life on the  edge,  until  he  finally
     found the perfect match, which has changed many things drammatically:
     he is not single anymore, and now has his first child (he  definitely
     knows  of);  on  28  January  2000  Daniel Tobias H. jumped into this
     world. He even got married on November 5th 1999.  If  you  like  this
     kind   of   stuff,   have   a   look   at  the  wedding  pictures  at

     Well, so far so good. He is still known to  reject  job  offers  that
     come  bundled  with Porsches and still doesn't own a BMW Z3 roadster,
     for he recently purchased a red 1996 Ford Probe Medici, enjoying life
     at 230 kph, while listening to the formidable 1975 KANSAS song ``Born
     On Wings Of Steel.''

     He still doesn't live in  Surrey,  but  in  Dortmund  in  a  knight's
     castle,  which was build in the 16th century and rebuild in the early
     90ies.  The building with its  tower,  park  and  pond  is  known  as
     Rittergut ``Haus Soelde''.

     Nothing really worth listing here.
 David Beasley,
     was  born  in London, England in 1961. He was educated at Southampton
     University where he read (for good reasons) Electronic Engineering.

     After spending several years at sea, he went  to  the  Department  of
     Computing  Mathematics  of the University of Wales, Cardiff, where he
     studied ARTIFICIAL INTELLIGENCE for a year. He then went on to  write
     a  thesis  on  GAs applied to Digital Signal Processing, and tried to
     break Joke's publications record.

     Since a very special event, he has taken over writing this  FAQ,  and
     consequently  started signing everything with ``The FAQmaster'' (He's
     had severe problems taking life seriously for some time before  that,
     however.) He likes Woody Allen's movies, English clothing of medieval
     times, especially Marks and Spencer, hates McDonald's Hamburgers, but
     occasionally  dives into the analysis of complex systems of any kind,
     (but prefers those with pedals and handlebars) and the books  by  (of
     course) Douglas Adams.

     He  is  not  married,  has no children, and also also doesn't live in

     He spent several years working for a  (mostly  interesting)  software
     company,  Praxis in Bath, England. He left after it became clear that
     the new owners, Deloitte and Touche,  had  no  interest  in  software
     engineering.   He now works for ingenta, a company which provides on-
     line access to learned publications and  other  on-line  services  to
     academic  users  around the world. This includes the long-established
     BIDS reference services.  ingenta  (  )  are
     based at Bath University, England.

     A  number  of  publications  related to GENETIC ALGORITHMs.  The most
     notable ones being:

     A Sequential Niche Technique for  Multimodal  Function  Optimization,
     Evolutionary  Computation,  1(2)  pp  101-125,  1993.  Available from

     Reducing Epistasis in Combinatorial Problems by Expansive Coding,  in
     S. Forrest (ed), Proceedings of the Fifth International Conference on
     Genetic Algorithms, Morgan-Kaufmann,  pp  400-407,  1993.   Available

     An  Overview  of Genetic Algorithms: Part 1, Fundamentals, University
     Computing, 15(2) pp 58-69, 1993.  Alailable from ENCORE  (See  Q15.3)
     in         file:         GA/papers/        or        from

     An  Overview  of  Genetic  Algorithms:  Part  2,   Research   Topics,
     University  Computing, 15(4) pp 170-181, 1993.  Available from Encore
     (See   Q15.3)   in    file:    GA/papers/    or    from

			       THAT'S ALL FOLKS!

	"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 frets his hour upon the stage;
					       and then is heared no more;
					   it is a tale; told by an idiot,
						   full of sound and fury,
						      signifying nothing."

						  --- Shakespeare, Macbeth


     Copyright  (c) 1993-2000 by J. Heitkoetter and D. Beasley, all rights

     This FAQ may be posted to any USENET newsgroup, on-line  service,  or
     BBS  as  long  as  it  is  posted  in  its entirety and includes this
     copyright statement.  This FAQ may not be distributed  for  financial
     gain.   This  FAQ  may  not  be included in commercial collections or
     compilations without express permission from the author.

End of ai-faq/genetic/part6


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