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
LICENSE:
MIT license.