Q1.5: What's Genetic Programming (GP)?

     GENETIC PROGRAMMING is the extension of the genetic model of learning
     into the space of programs. That is, the objects that constitute  the
     POPULATION   are  not  fixed-length  character  strings  that  encode
     possible solutions to the problem at hand, they  are  programs  that,
     when  executed,  "are"  the candidate solutions to the problem. These
     programs are expressed in genetic programming as parse trees,  rather
     than  as lines of code.  Thus, for example, the simple program "a + b
     * c" would be represented as:

		 +
		/ \
		a  *
		 / \
		 b  c

     or, to be precise, as suitable data  structures  linked  together  to
     achieve this effect. Because this is a very simple thing to do in the
     programming language Lisp, many GPers tend to use Lisp. However, this
     is simply an implementation detail. There are straightforward methods
     to implement GP using a non-Lisp programming environment.

     The programs in the POPULATION are  composed  of  elements  from  the
     FUNCTION  SET and the TERMINAL SET, which are typically fixed sets of
     symbols selected to be appropriate to the solution of problems in the
     domain of interest.

     In  GP  the  CROSSOVER  operation  is  implemented by taking randomly
     selected subtrees in the INDIVIDUALs (selected according to  FITNESS)
     and exchanging them.

     It should be pointed out that GP usually does not use any MUTATION as
     a GENETIC OPERATOR.

     More information is available in the GP mailing  list  FAQ.   (See  Q
     15.2)

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End of ai-faq/genetic/part2
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