Hi all,

I'm pleased to announce the first public release of charlie, a genetic
algorithms library for Ruby.

## FEATURES:
- Quickly develop GAs by combining several parts (genotype, selection,
crossover, mutation) provided by the library.
- Sensible defaults are provided with any genotype, so often you only
need to define a fitness function.
- Easily replace any of the parts by your own code.
- Test different strategies in GA, and generate reports comparing them.

## EXAMPLE:  (also at http://pastie.caboo.se/130559 with better formatting)
This example finds the binary representation of the number 512.

 require 'rubygems'
 require 'charlie'
 class Find512 < BitStringGenotype(10) # choose a genotype, in this
case a list of 10 bits represents a solution
   # Define a fitness function. This one returns minus the offset to
the best solution, so a higher number is better.
   # Usually, you won't know the best solution, and will define this
as some value that needs to be maximized.
   def fitness
     # Use the 'genes' function to retrieve the array of bits
representing this solution.
     -(genes.map(&:to_s).join.to_i(2) - 512).abs
   end
 end
 # Finally, create an instance of a population (with the default size
of 20) and let it run for the default number of 100 generations.
 Population.new(Find512).evolve_on_console

## RUBYQUIZ #142 SOLUTION:
I know, it's a bit late. ;)

 require 'rubygems'
 require 'charlie'
 N=5
 CITIES = (0...N).map{|i| (0...N).map{|j| [i,j] } }.inject{|a,b|a+b}
 class TSP < PermutationGenotype(CITIES.size)
   def fitness
     d=0
     (genes + [genes[0]]).each_cons(2){|a,b|
        a,b=CITIES[a],CITIES[b]
        d += Math.sqrt( (a[0]-b[0])**2 + (a[1]-b[1])**2 )
      }
     -d # lower distance -> higher fitness.
   end
 end
 pop = Population.new(TSP,20).evolve_on_console(50)

Several other simple examples are included in the gem/tarball.

## INSTALLATION:
* sudo gem install charlie

## Links
* http://rubyforge.org/projects/charlie/
* http://charlie.rubyforge.org

## LICENSE:
MIT license.