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Evolutionary Algorithm Overview

Algorithms using evolutionary computation are called evolutionary algorithms (EAs).  These algorithms apply mechanisms of evolution such as selection, mutation, and recombination (sex) to a population of individual computer programs in order to evolve better program solutions to a problem without a human designing them. 

Evolutionary algorithms generally use a few basic steps to evolve programs. 

(1) a population is filled with either completely random programs or with programs that have some basic functionality designed by the user. 

(2) each generation the programs are subjected to the following
   
a.  selection: a set of the best current programs are chosen, where "best" is based on a user-specified desired behavior.  Examples of desired behavior range from simple programs that best solve a math equation to complex programs which best control a robot to navigate a lanscape

b.  random changes:  the chosen "best set" of programs undergoes mutation and crossover.  Mutation is often as simple as changing one line of code in the program to a random line.  Crossover involves splitting 2 programs in one or more places, and combining pieces of the 2 different programs to create new programs.

c.  the modified "best" programs are placed back in the population, as well as enough copies to fill the population back to its original level.  The better a program is the more copies of itself get placed in the new population.

3) At some point the algorithm is stopped and the best program(s) of the current population are given to the user.
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