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genetic algorithms
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| #!/usr/bin/env python3 | |
| # G E N E T I C A L G O R I T H M S | |
| # Project Genetic Algorithm with Python | |
| # Author Barnabas Markus | |
| # Email barnabasmarkus@gmail.com | |
| # Date 29.02.2016 | |
| # Python 3.5.1 | |
| # License MIT | |
| import math | |
| import random | |
| class Population: | |
| def __init__(self, popsize=1000, veclength=20, domain=[(100,200)]): | |
| # Population Parameters | |
| self.popsize = popsize | |
| self.veclength = veclength | |
| self.domain = domain * veclength | |
| self.population = self.create_population() | |
| def create_population(self): | |
| """ Build Initial Population """ | |
| population = [self.create_vector() for _ in range(self.popsize)] | |
| return population | |
| def create_vector(self): | |
| """ Create Random Vector """ | |
| vec = [random.randint(self.domain[i][0],self.domain[i][1]) | |
| for i in range(self.veclength)] | |
| return vec | |
| def get_ranked_population(self): | |
| """ Get Score of Population, Rank Population based on Scores """ | |
| pop_score = [(self.get_vec_score(vec),vec) for vec in self.population] | |
| pop_score.sort() | |
| ranked_pop = [vec for (score,vec) in pop_score] | |
| # Print current best | |
| # print(pop_score[0][0]) | |
| return ranked_pop | |
| @staticmethod | |
| def get_vec_score(vec): | |
| """ Get Score of Vector """ | |
| vec_score = 0 | |
| for v in vec: | |
| vec_score += (v + 0.1) * math.e / 3 | |
| return int(vec_score) | |
| def evolve(self, elite=0.2, maxiter=20, mutprob=0.4): | |
| """ Evolve Population """ | |
| # Evolve Parameters | |
| self.elite = elite | |
| self.maxiter = maxiter | |
| self.mutprob = mutprob | |
| # topelite: num of survivors of a generation | |
| self.topelite = int(elite * self.popsize) | |
| for i in range(self.maxiter): | |
| ranked_pop = self.get_ranked_population() | |
| self.population = ranked_pop[0:self.topelite] | |
| # Fill Population with Survivors Muted and Crossovered Forms | |
| while len(self.population) < self.popsize: | |
| if random.random() < self.mutprob: | |
| # Mutation | |
| c = random.randint(0,self.topelite-1) | |
| self.population.append(self.mutate(self.population[c])) | |
| else: | |
| # Crossover | |
| c1 = random.randint(0,self.topelite-1) | |
| c2 = random.randint(0,self.topelite-1) | |
| self.population.append( | |
| self.crossover(self.population[c1], self.population[c2])) | |
| # Return Optimal Vector | |
| return self.population[0] | |
| def mutate(self, r): | |
| """ Mutate Vector """ | |
| i = random.randint(0,self.veclength-1) | |
| x = random.randint(self.domain[i][0],self.domain[i][1]) | |
| r[i] = x | |
| return r | |
| def crossover(self, r1, r2): | |
| """ Crossover 2 Vectors """ | |
| i = random.randint(1,self.veclength-2) | |
| return r1[0:i] + r2[i:] |
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