if scores[i] < b_fitness:
b_fitness = scores[i]
b_gene = gene
children = []
for i in range(g.n_pop / 2):
p1 = g.t_selection(scores)
p2 = g.t_selection(scores)
ch1, ch2 = g.u_crossover(g.population[p1], g.population[p1])
children.append(g.mutation(ch1))
children.append(g.mutation(ch2))
g.population = children
print "f1(x) = %f" % b_fitness
# TEST NUM. 2 -- f2(x1, x2)
g = GA(n_gen, n_pop, p_mut, p_xover, l_dna * 2)
b_gene = ""
b_fitness = 999999
g.init_population()
for _ in range(g.n_gen):
