I wrote a bunch of ruby code for a book project I've just finished. One criticism is that it is fine code but not very "ruby like". I agree my style was simplified for communication reasons, and although it's procedural code, it still feels "ruby-like" to me.

For the representative example below, any ideas on making it more "ruby like"?

I wrote a bunch of Ruby code for a book project I've just finished. One criticism is that it is fine code but not very "ruby like". I agree my style was simplified for communication reasons, and although it's procedural code, it still feels "ruby-like" to me.

For the representative example below, any ideas on making it more "Ruby like"?

# Genetic Algorithm in the Ruby Programming Language

# The Clever Algorithms Project: http://www.CleverAlgorithms.com
# (c) Copyright 2010 Jason Brownlee. Some Rights Reserved. 
# This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.5 Australia License.

def onemax(bitstring)
  sum = 0
  bitstring.size.times {|i| sum+=1 if bitstring[i].chr=='1'}
  return sum
end

def random_bitstring(num_bits)
  return (0...num_bits).inject(""){|s,i| s pop[j][:fitness]) ? pop[i] : pop[j]
end

def point_mutation(bitstring, rate=1.0/bitstring.size)
  child = ""
   bitstring.size.times do |i|
     bit = bitstring[i].chr
     child =rate
  point = 1 + rand(parent1.size-2)
  return parent1[0...point]+parent2[point...(parent1.size)]
end

def reproduce(selected, pop_size, p_cross, p_mutation)
  children = []  
  selected.each_with_index do |p1, i|
    p2 = (i.modulo(2)==0) ? selected[i+1] : selected[i-1]
    p2 = selected[0] if i == selected.size-1
    child = {}
    child[:bitstring] = crossover(p1[:bitstring], p2[:bitstring], p_cross)
    child[:bitstring] = point_mutation(child[:bitstring], p_mutation)
    children = pop_size
  end
  return children
end

def search(max_gens, num_bits, pop_size, p_crossover, p_mutation)
  population = Array.new(pop_size) do |i|
    {:bitstring=>random_bitstring(num_bits)}
  end
  population.each{|c| c[:fitness] = onemax(c[:bitstring])}
  best = population.sort{|x,y| y[:fitness]  x[:fitness]}.first  
  max_gens.times do |gen|
    selected = Array.new(pop_size){|i| binary_tournament(population)}
    children = reproduce(selected, pop_size, p_crossover, p_mutation)    
    children.each{|c| c[:fitness] = onemax(c[:bitstring])}
    children.sort!{|x,y| y[:fitness]  x[:fitness]}
    best = children.first if children.first[:fitness] >= best[:fitness]
    population = children
    puts " > gen #{gen}, best: #{best[:fitness]}, #{best[:bitstring]}"
    break if best[:fitness] == num_bits
  end
  return best
end

if __FILE__ == $0
  # problem configuration
  num_bits = 64
  # algorithm configuration
  max_gens = 100
  pop_size = 100
  p_crossover = 0.98
  p_mutation = 1.0/num_bits
  # execute the algorithm
  best = search(max_gens, num_bits, pop_size, p_crossover, p_mutation)
  puts "done! Solution: f=#{best[:fitness]}, s=#{best[:bitstring]}"
end

# Genetic Algorithm in the Ruby Programming Language

# The Clever Algorithms Project: http://www.CleverAlgorithms.com
# (c) Copyright 2010 Jason Brownlee. Some Rights Reserved. 
# This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 2.5 Australia License.

def onemax(bitstring)
  sum = 0
  bitstring.size.times {|i| sum+=1 if bitstring[i].chr=='1'}
  return sum
end

def random_bitstring(num_bits)
  return (0...num_bits).inject(""){|s,i| s<<((rand<0.5) ? "1" : "0")}
end

def binary_tournament(pop)
  i, j = rand(pop.size), rand(pop.size)
  j = rand(pop.size) while j==i
  return (pop[i][:fitness] > pop[j][:fitness]) ? pop[i] : pop[j]
end

def point_mutation(bitstring, rate=1.0/bitstring.size)
  child = ""
   bitstring.size.times do |i|
     bit = bitstring[i].chr
     child << ((rand()<rate) ? ((bit=='1') ? "0" : "1") : bit)
  end
  return child
end

def crossover(parent1, parent2, rate)
  return ""+parent1 if rand()>=rate
  point = 1 + rand(parent1.size-2)
  return parent1[0...point]+parent2[point...(parent1.size)]
end

def reproduce(selected, pop_size, p_cross, p_mutation)
  children = []  
  selected.each_with_index do |p1, i|
    p2 = (i.modulo(2)==0) ? selected[i+1] : selected[i-1]
    p2 = selected[0] if i == selected.size-1
    child = {}
    child[:bitstring] = crossover(p1[:bitstring], p2[:bitstring], p_cross)
    child[:bitstring] = point_mutation(child[:bitstring], p_mutation)
    children << child
    break if children.size >= pop_size
  end
  return children
end

def search(max_gens, num_bits, pop_size, p_crossover, p_mutation)
  population = Array.new(pop_size) do |i|
    {:bitstring=>random_bitstring(num_bits)}
  end
  population.each{|c| c[:fitness] = onemax(c[:bitstring])}
  best = population.sort{|x,y| y[:fitness] <=> x[:fitness]}.first  
  max_gens.times do |gen|
    selected = Array.new(pop_size){|i| binary_tournament(population)}
    children = reproduce(selected, pop_size, p_crossover, p_mutation)    
    children.each{|c| c[:fitness] = onemax(c[:bitstring])}
    children.sort!{|x,y| y[:fitness] <=> x[:fitness]}
    best = children.first if children.first[:fitness] >= best[:fitness]
    population = children
    puts " > gen #{gen}, best: #{best[:fitness]}, #{best[:bitstring]}"
    break if best[:fitness] == num_bits
  end
  return best
end

if __FILE__ == $0
  # problem configuration
  num_bits = 64
  # algorithm configuration
  max_gens = 100
  pop_size = 100
  p_crossover = 0.98
  p_mutation = 1.0/num_bits
  # execute the algorithm
  best = search(max_gens, num_bits, pop_size, p_crossover, p_mutation)
  puts "done! Solution: f=#{best[:fitness]}, s=#{best[:bitstring]}"
end