
 GENETIC ALGORITHMS
 ==================

 The goal of this module is to show how GA's explore a
 search   space  through  the   guidance  an arbitrary
 fitness   function.  When    complex  functions   are
 optimized, researchers often  use exact algorithms or
 at least heuristics that contain the domain knowledge
 to  guide the search.  GA's  use   (in most cases) no
 domain knowledge at all. That  makes it  robust at  a
 search through a discontinous domain.

 This document gives a  brief introduction to  genetic
 algorithms  (hence   abbreviated  as  GA). GA's   are
 powerful search  algorithms based on the mechanics of
 natural selection and natural genetics.

 They evolve a good point  in the search  space trough
 the   survival of the fittest  among  a population of
 strings and a exploration  of the search space trough
 random  mutation    and recombination  of  the search
 strings.

 These  algorithms   are   computationally  simple yet
 powerful in   their   search   for       improvement.
 Furthermore  they are  not  fundamentally limited  by
 restrictive    assumptions  about  the   search space
 (assumptions  concerning continuity,  existence    of
 derivatives, unimodality and other matters).

 GA's  are  theoretically  and  empirically  proven to
 provide robust search in complex spaces.

 SEE ALSO
 ========

 David   Goldberg, "Genetic   Algorithms  in   Search,
 Optimization,  and Machine  Learning", Addison-Wesley
 1989

