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FAQ: comp.ai.genetic part 3/6 (A Guide to Frequently Asked Questions)
Section - Q4: How many EAs exist? Which?

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 The All Stars
     There  are  currently  3  main  paradigms  in  EA  research:  GENETIC
     ALGORITHMs,   EVOLUTIONARY  PROGRAMMING,  and  EVOLUTION  STRATEGIEs.
     CLASSIFIER SYSTEMs and GENETIC PROGRAMMING are OFFSPRING  of  the  GA
     community.   Besides  this  leading  crop,  there  are numerous other
     different approaches, alongside hybrid experiments, i.e. there  exist
     pieces  of software residing in some researchers computers, that have
     been described in papers in conference proceedings, and  may  someday
     prove  useful  on certain tasks. To stay in EA slang, we should think
     of these evolving strands as BUILDING BLOCKs,  that  when  recombined
     someday,  will  produce  new  offspring  and  give  birth  to  new EA
     paradigm(s).

     One such interesting offspring is the Memetic Algorithm.  This  is  a
     hybrid  evolutionary  algorithm,  which  makes  use  of  local search
     operators.               For               details,               see
     http://www.densis.fee.unicamp.br/~moscato/memetic_home.html

 Promising Rookies
     As  far  as  "solving complex function and COMBINATORIAL OPTIMIZATION
     tasks" is concerned, Davis' work on real-valued  representations  and
     adaptive operators should be mentioned (Davis 89). Moreover Whitley's
     Genitor system incorporating ranking  and  "steady  state"  mechanism
     (Whitley    89),    Goldberg's   "messy   GAs",   involves   adaptive
     representations (Goldberg 91), and Eshelman's CHC algorithm (Eshelman
     91).   For  real  FUNCTION OPTIMIZATION, Differential EVOLUTION seems
     hard to beat in terms of convergence speed  as  well  as  simplicity:
     With just three control variables, tuning is particularly easy to do.

     For  "the  design  of  robust  learning  systems",  i.e.  the   field
     characterized  by  CFS,  Holland's (1986) CLASSIFIER SYSTEM, with its
     state-of-the-art implementation CFS-C  (Riolo  88),  we  should  note
     developments  in  SAMUEL  (Grefenstette  89), GABIL (De Jong & Spears
     91), and GIL (Janikow 91).

     References

     Davis,  L.  (1989)  "Adapting  operator  probabilities   in   genetic
     algorithms", [ICGA89], 60-69.

     De  Jong  K.A.  &  Spears  W. (1991) "Learning concept classification
     rules using genetic algorithms". Proc. 12th IJCAI,  651-656,  Sydney,
     Australia: Morgan Kaufmann.

     Dorigo  M.  &  E.  Sirtori (1991)."ALECSYS: A Parallel Laboratory for
     Learning Classifier Systems". Proceedings of the Fourth International
     Conference  on  Genetic  Algorithms, San Diego, California, R.K.Belew
     and L.B.Booker (Eds.), Morgan Kaufmann, 296-302.

     Dorigo M. (1995). "ALECSYS and the AutonoMouse: Learning to Control a
     Real  Robot by Distributed Classifier Systems". Machine Learning, 19,
     3, 209-240.

     Eshelman, L.J. et al. (1991)  "Preventing  premature  convergence  in
     genetic algorithms by preventing incest", [ICGA91], 115-122.

     Goldberg,  D. et al. (1991) "Don't worry, be messy", [ICGA91], 24-30.

     Grefenstette, J.J. (1989) "A system for learning  control  strategies
     with genetic algorithms", [ICGA89], 183-190.

     Holland,  J.H.  (1986)  "Escaping  brittleness:  The possibilities of
     general-purpose learning algorithms applied  to  parallel  rule-based
     systems".   In R. Michalski, J. Carbonell, T. Mitchell (eds), Machine
     Learning: An Artificial  Intelligence  Approach.  Los  Altos:  Morgan
     Kaufmann.

     Janikow   C.  (1991)  "Inductive  learning  of  decision  rules  from
     attribute-based examples:  A  knowledge-intensive  Genetic  Algorithm
     approach". TR91-030, The University of North Carolina at Chapel Hill,
     Dept. of Computer Science, Chapel Hill, NC.

     Riolo,  R.L.  (1988)  "CFS-C:  A  package   of   domain   independent
     subroutines  for  implementing classifier systems in arbitrary, user-
     defined  environments".   Logic  of  computers  group,  Division   of
     computer science and engineering, University of Michigan.

     Whitley,  D.  et  al.  (1989)  "The  GENITOR  algorithm and selection
     pressure: why rank-based allocation of reproductive trials is  best",
     [ICGA89], 116-121.

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Top Document: FAQ: comp.ai.genetic part 3/6 (A Guide to Frequently Asked Questions)
Previous Document: Q3: Who is concerned with EAs?
Next Document: Q4.1: What about Alife systems, like Tierra and VENUS?

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