python - Project Euler 461 - Genetic Algorithm -


someone told me this problem should easy solve genetic algorithm.

i read stuff topic (i hadn't heard before), , wrote (and copied) code.
results close optimum, not close enough.

i'd have it:

import time import math import random  def f(n, k):     return math.exp(k / n) - 1  def individual(length, min, max):     'create member of population.'     return [random.randint(min, max) x in range(length)]  def population(count, length, min, max):     """     create number of individuals (i.e. population).      count: number of individuals in population     length: number of values per individual     min: minimum possible value in individual's list of values     max: maximum possible value in individual's list of values      """     return [individual(length, min, max) x in range(count)]  def fitness(individual, target):     def get_best_last_element(a, b, c):         s = math.pi - f(eu461.base, a) - f(eu461.base, b) - f(eu461.base, c)         s += 1          if s > 1:             return round(math.log(s) * eu461.base)         else:             return 0      def getg():         return get_best_last_element     """     determine fitness of individual. higher better.      individual: individual evaluate     target: target number individuals aiming     """     l = get_best_last_element(individual[0], individual[1], individual[2])      return abs(target - sum([f(eu461.base, k) k in individual]) - f(eu461.base, l))  def grade(pop, target):     'find average fitness population.'     return sum([fitness(x, target) x in pop]) / (len(pop))  def evolve(pop, target, retain=0.2, random_select=0.05, mutate=0.01):     graded = [(fitness(x, target), x) x in pop]     graded = [x[1] x in sorted(graded)]      retain_length = int(len(graded) * retain)     parents = graded[:retain_length]      # randomly add other individuals     # promote genetic diversity     individual in graded[retain_length:]:         if random_select > random.random():             parents.append(individual)      # mutate individuals     individual in parents:         if mutate > random.random():             pos_to_mutate = random.randint(0, len(individual) - 1)              # mutation not ideal, because             # restricts range of possible values,             # function unaware of min/max             # values used create individuals,             individual[pos_to_mutate] = random.randint(min(individual), max(individual))      # crossover parents create children     parents_length = len(parents)     desired_length = len(pop) - parents_length     children = []     while len(children) < desired_length:         male = random.randint(0, parents_length - 1)         female = random.randint(0, parents_length - 1)         if male != female:             male = parents[male]             female = parents[female]             half = len(male) // 2             if random.randint(0, 1):                 child = male[:half] + female[half:]             else:                 child = female[:half] + male[half:]             children.append(child)     parents.extend(children)      return parents  def get_best_last_element(a, b, c):     s = math.pi - f(eu461.base, a) - f(eu461.base, b) - f(eu461.base, c)     s += 1      if s > 0:         return round(math.log(s) * eu461.base)     else:         return 0  def eu461():     target = math.pi     p_count = 10000     i_length = 3     i_min = 0     i_max = round(eu461.base * math.log(math.pi + 1))      p = population(p_count, i_length, i_min, i_max)     fitness_history = [grade(p, target),]      in range(150):         p = evolve(p, target)         fitness_history.append(grade(p, target))      datum in fitness_history:         pass #print (datum)      return p[0], get_best_last_element(p[0][0], p[0][1], p[0][2]), sum([f(eu461.base, k) k in p[0]]) + f(eu461.base, get_best_last_element(p[0][0], p[0][1], p[0][2])) eu461.base = 200  if __name__ == "__main__":     starttime = time.clock()     print (eu461())     elapsedtime = time.clock() - starttime     print ("time spent in (", __name__, ") is: ", elapsedtime, " sec")


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