134 lines
4.3 KiB
Python
134 lines
4.3 KiB
Python
import random
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from itertools import permutations
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# genetic algorithm search of the one max optimization problem
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from numpy.random import randint
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from numpy.random import rand
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# this is helper function for sum_distance function, it counts the taxi cab distance between 2 vectors [x,y]
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def distance(x,y):
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temp1 = abs(x[0]-y[0])
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temp2 = abs(x[1]-y[1])
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vector_distance = temp1+temp2
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return vector_distance
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# this is fitting function which tells how well specimen fits the environment
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# this function counts the sum of distances between vectors for a specimen
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# this was just for testing, it should be probably changed to A*
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def sum_distance(speciment):
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sum = 0
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for i in range(0,len(speciment)-2):
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pom = distance(speciment[i],speciment[i+1])
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sum = sum + pom
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return sum
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# tournament selection
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# this function randomly puts speciments to groups, from each group one best speciment is taken for reproduction
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def selection(pop, scores, k=3):
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# first random selection
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selection_ix = randint(len(pop))
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for ix in randint(0, len(pop), k- 1):
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# check if better (e.g. perform a tournament)
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if scores[ix] < scores[selection_ix]:
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selection_ix = ix
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return pop[selection_ix]
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# crossover two parents to create two children
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# this function creates speciments for new generation
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def crossover(p1, p2, r_cross):
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p1=list(p1)
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p2=list(p2)
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# children are copies of parents by default
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c1, c2 = p1.copy(), p2.copy()
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# check for recombination
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if rand() < r_cross:
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# select crossover point that is not on the end of the string
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pt = randint(1, len(p1) - 2)
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# perform crossover
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temp1 = p1[:pt]
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for mine in p2:
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if mine not in p1[:pt]:
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temp1.append(mine)
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temp2 = p2[:pt]
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for mine in p1:
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if mine not in p2[:pt]:
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temp2.append(mine)
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c1 = temp1
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c2 = temp2
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return [c1, c2]
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# mutation operator
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# this function checks whether genes mutated
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# if gene is mutated then it is swapped with randomly chosen gene from the same speciment
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def mutation(speciment, r_mut):
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for i in range(len(speciment)-1):
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# check for a mutation
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if rand() < r_mut:
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# flip the bit
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temp = speciment[i]
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pom = random.randint(0,len(speciment)-1)
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speciment[i] = speciment[pom]
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speciment[pom] = temp
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# genetic algorithm
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def genetic_algorithm(objective, n_iter, n_pop, r_cross, r_mut):
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# this is hardcoded list of coordinates of all mines (for tests only) which represents one speciment in population
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# it is then permutated to get set number of species and create population
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speciment=[[1,1],[5,2],[1,3],[2,5],[2,1],[3,2],[3,4],[5,0]]
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pop = [random.choice(list(permutations(speciment,len(speciment)))) for _ in range(n_pop)]
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# permutation function returns tuples so I change them to lists
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for i in range(len(pop)):
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pop[i] = list(pop[i])
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# keep track of best solution
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best, best_eval = 0, objective(pop[0])
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# enumerate generations
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for gen in range(n_iter):
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# evaluate all candidates in the population
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scores = [objective(c) for c in pop]
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# check for new best solution
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for i in range(n_pop):
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if scores[i] < best_eval:
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best, best_eval = pop[i], scores[i]
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print(">%d, new best f(%s) = %.3f" % (gen, pop[i], scores[i]))
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# select parents
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selected = [selection(pop, scores) for _ in range(n_pop)]
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# create the next generation
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children = list()
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for i in range(0, n_pop, 2):
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# get selected parents in pairs
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p1, p2 = selected[i], selected[i + 1]
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# crossover and mutation
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for c in crossover(p1, p2, r_cross):
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# mutation
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mutation(c, r_mut)
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# store for next generation
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children.append(c)
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# replace population
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pop = children
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return [best, best_eval]
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# define the total iterations
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n_iter = 100
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# bits
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n_bits = 20
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# define the population size
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n_pop = 100
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# crossover rate
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r_cross = 0.9
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# mutation rate
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r_mut = 0.05
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# perform the genetic algorithm search
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best, score = genetic_algorithm(sum_distance, n_iter, n_pop, r_cross, r_mut)
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print('Done!')
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print('f(%s) = %f' % (best, score))
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