Evolutionary Algorithm

'''Evolutionary Algorithm''' (also EA, evolutionary computation, artificial evolution) 
is a generic term used to indicate any population-based heuristic optimization 
algorithm that uses mechanisms inspired by biological [[Evolution|evolution]], such as 
reproduction, mutation and recombination. Candidate solutions to the 
optimization problem play the role of individuals in a population, and the cost 
function determines the environment within which the solutions "live" (see 
also fitness function). Evolution of the population then takes place after the 
repeated application of the above operators.

*[[Genetic Algorithms]] - This is the most popular type of EA. One seeks the solution of a problem in the form of strings of numbers or characters (traditionally binary), virtually applying recombination operators in addition to selection and mutation
*[[Genetic Programming]] - Here the solutions are in the form of computer programs, and their fitness is determined by their ability to solve a computational problem;

The major difference between [[Genetic Algorithms]] and [[Genetic Programming]] is
the target: in '''Genetic Algorithms''' you try to find a certain
blueprint or abstract code in form of strings, in '''Genetic Programming''' you
try to change a computer language code directly (for example a suitable
language like Lisp).

== Links ==

* [http://ls11-www.cs.uni-dortmund.de/people/beyer/EA-glossary/def-engl-html.html Glossary of Terms and Definitions in Evolutionary Algorithms]

* [http://www.econ.iastate.edu/tesfatsi/holland.GAIntro.htm Introduction to Genetic Algorithms] by John Holland