lua genetic algorithm

function table_print (tt, indent, done)
  done = done or {}
  indent = indent or 0
  if type(tt) == "table" then
    local sb = {}
    for key, value in pairs (tt) do
      table.insert(sb, string.rep (" ", indent)) -- indent it
      if type (value) == "table" and not done [value] then
        done [value] = true
        table.insert(sb, key .. " = {\n");
        table.insert(sb, table_print (value, indent + 2, done))
        table.insert(sb, string.rep (" ", indent)) -- indent it
        table.insert(sb, "}\n");
      elseif "number" == type(key) then
        table.insert(sb, string.format("\"%s\"\n", tostring(value)))
      else
        table.insert(sb, string.format(
            "%s = \"%s\"\n", tostring (key), tostring(value)))
       end
    end
    return table.concat(sb)
  else
    return tt .. "\n"
  end
end

function to_string( tbl )
    if  "nil"       == type( tbl ) then
        return tostring(nil)
    elseif  "table" == type( tbl ) then
        return table_print(tbl)
    elseif  "string" == type( tbl ) then
        return tbl
    else
        return tostring(tbl)
    end
end

function create_population(pop_size, gene_len)
  local population = {}
  for i = 1, pop_size do
    local individual = {}
    for j = 1, gene_len do
      individual[j] = math.random(0, 1)
    end
    population[i] = individual
  end
  return population
end

function evaluate_fitness(pop)
  local fitness = {}
  for i, ind in ipairs(pop) do
    local sum = 0
    for j, gene in ipairs(ind) do
      sum = sum + gene
    end
    fitness[i] = sum
  end
  return fitness
end

function select_parents(pop, fit)
  local parents = {}
  for i = 1, #pop do
    local p1 = math.random(1, #pop)
    local p2 = math.random(1, #pop)
    if fit[p1] > fit[p2] then
      parents[i] = pop[p1]
    else
      parents[i] = pop[p2]
    end
  end
  return parents
end

function crossover(par, pop_size, gene_len)
  local offspring = {}
  for i = 1, pop_size, 2 do
    local p1 = par[i]
    local p2 = par[i+1]
    local o1 = {}
    local o2 = {}
    for j = 1, gene_len do
      if math.random(0, 1) == 0 then
        o1[j] = p1[j]
        o2[j] = p2[j]
      else
        o1[j] = p2[j]
        o2[j] = p1[j]
      end
    end
    offspring[i] = o1
    offspring[i+1] = o2
  end
  return offspring
end

function mutation(off, mut_rate, gene_len)
  for i, ind in ipairs(off) do
    for j = 1, gene_len do
      if math.random() < mut_rate then
        ind[j] = 1 - ind[j]
      end
    end
  end
  return off
end

function genetic_algorithm(pop_size, gene_len, mut_rate, generations)
  local pop = create_population(pop_size, gene_len)
  for i = 1, generations do
    local fit = evaluate_fitness(pop)
    local par = select_parents(pop, fit)
    local off = crossover(par, pop_size, gene_len)
    pop = mutation(off, mut_rate, gene_len)
  end
  return pop
end

result = genetic_algorithm(8, 4, 0.01, 100)

print(to_string(result))