Il Fri, 06 Mar 2009 14:13:47 -0800, Scott David Daniels ha scritto: > mattia wrote: >> Here is my last shot, where I get rid of all the old intermediate >> functions: >> >> def selection(fitness, population): >> lp = len(population) >> roulette_wheel = [] >> for x in population: >> roulette_wheel += [x]*fitness(x) >> selected_population = [[]]*lp >> selected_population[:2] = sorted(population, key=fitness, >> reverse=True)[:2] >> selected_population[2:] = [choice(roulette_wheel) for _ in range >> (lp-2)] > Try something like this to choose likely couples: > > import random > import bisect > > def choose_pairs(fitness_population, decider=random): > '''Pick and yield pairs weighted by fitness for crossing. > > We assume weighted_population has fitness already calculated. > decide is a parameter to allow testing. ''' > total = 0 > cumulative = [] > candidates = [] > for fitness, individual in set(fitness_population): > # calculate total weights, extract real candidates if > fitness > 0: > total += fitness > cumulative.append(total) > candidates.append(individual) > assert len(candidates) > 1 > while True: > # pick a candidate by weight > c0 = decider.random() * total > first = bisect.bisect_left(cumulative, c0) if first: > weighting = cumulative[first] - cumulative[first - 1] > else: > weighting = cumulative[0] > # pick another distinct candidate by fitness c1 = choice = > decider.random() * (total - weighting) if choice >= > cumulative[first] - weighting: > choice += weight # adjust to avoid selecting first > second = bisect.bisect_left(cumulative, choice) yield > candidates[first], candidates[second] > > --Scott David Daniels > scott.dani...@acm.org
Thanks, I've found another solution here: http://www.obitko.com/tutorials/ genetic-algorithms/selection.php so here is my implementation: def create_chromosome(min, max, length): return [randint(min, max) for i in range(length)] def create_population(nelem, min, max, length): # preconditions: nelem > 1 and nelem is even if not nelem > 1: nelem = 2 if not nelem%2 == 0: print("The population must have an even number of elements. Correcting...") nelem += 1 return [create_chromosome(min, max, length) for i in range(nelem)] def get_fap(fitness, population): fap = [] total = 0 for x in population: f = fitness(x) fap += [(f, x)] total += f return sorted(fap, reverse=True), total def my_rw(): list, tot = get_fap(sum, pop) r = randint(0, tot-1) i = 0 print(r) for f, e in list: i += f print(i) if i > r: return e return [] # never reached if __name__ == "__main__": pop = create_population(5, 0, 1, 10) # selection_mat(sum, pop) #print(rw(sum, pop)) list, tot = get_fap(sum, pop) print(list) print(tot) for i in range(6): print(my_rw()) -- http://mail.python.org/mailman/listinfo/python-list
