class DE(): def __init__(self, model="DTLZ7"): self.top_bound = [3.6, 0.8, 28.0, 8.3, 8.3, 3.9, 5.5] self.bottom_bound = [2.6, 0.7, 17.0, 7.3, 7.3, 2.9, 5] self.f2_high = -10**6 self.f2_low = 10**6 self.f1_high = -10**6 self.f1_low = 10**6 self.evals = 0 self.median = [] if model == "Osyczka": self.model = Osyczka() elif model == "Golinski": self.model = Golinski() elif model == "Kursawe": self.model = Kursawe() elif model == "Schaffer": self.model = Schaffer() elif model == "DTLZ7": self.model = DTLZ7(10, 2) self.best_solution = Thing() self.best_solution.score = 0 self.best_solution.energy = 1 self.best_solution.have = [] self.previous_era = [] #Returns three different things that are not 'avoid'. def threeOthers(self, lst, avoid): def oneOther(): x = self.a(lst) while x in seen: x = self.a(lst) seen.append(x) return x # ----------------------- seen = list(avoid.have) this = oneOther() that = oneOther() theOtherThing = oneOther() return this, that, theOtherThing def a(self, lst): return lst[self.n(len(lst))] def n(self, number): return int(uniform(0, number)) def candidate(self): # something = [uniform(self.model.low(d), self.model.high(d)) # for d in self.model.decisions()] something = self.model.get_random_state() new = Thing() new.have = something new.score = self.model.energy(new.have) new.energy = self.model.energy(new.have) return new def run( self, maximum=100, # number of repeats np=100, # number of candidates f=0.75, # extrapolate amount cf=0.3, # prob of cross-over epsilon=0.01, s=0.1): print "asd" + str(np) frontier = [self.candidate() for _ in range(np)] print "test" median = [] print maximum for k in range(0, 100): total, n, output = self.update(f, cf, frontier) self.evals += n frontier.sort(key=lambda x: x.energy) #print output + "Frontier energy:" + str(total) + " Count:" + str(n) + " Max Energy:" + str(frontier[0].energy) + " Min Energy:" + str(frontier[len(frontier)-1].energy) min = frontier[0].energy max = frontier[len(frontier) - 1].energy median.append(frontier[int(len(frontier) / 2)].score) big = max - (max - min) * s / 100 new_frontier = [x for x in frontier if x.energy <= big] frontier = new_frontier if (n > 0 and total / n > (1 - epsilon)) or n <= 0 or len(frontier) < 3: break print "Median values:" print median return self.previous_era def update(self, f, cf, frontier, total=0.0, n=0): output = "" MAX_LIVES = 10 ERA_LENGTH = 10 era_List = [] current_era = [] lives = MAX_LIVES for x in frontier: s = x.energy new = self.extrapolate(frontier, x, f, cf) # if self.model.type1(x.have, new.have): # output += "." if s < new.energy: output += "." # elif self.model.type1(new.have, x.have): # x.energy = new.energy # x.score = new.score # x.have = new.have # output += "+" elif new.energy < s: x.energy = new.energy x.score = new.score x.have = new.have output += "+" # if self.model.type1(new.have, self.best_solution.have): # self.best_solution.score = new.score # self.best_solution.have = new.have # self.best_solution.energy = new.energy # self.best_solution.evals = self.evals + n # output += "!" if new.energy < self.best_solution.energy: self.best_solution.score = new.score self.best_solution.have = new.have self.best_solution.energy = new.energy self.best_solution.evals = self.evals + n output += "!" if len(output) == 50: print output + " Best Solution: [", for a in self.best_solution.have: print("%.2f " % a), print "] Energy: " + str( self.best_solution.score) + " Evals: " + str(self.evals + n) current_era.append(x.have) if len(current_era) == ERA_LENGTH and len(era_List) > 0: increment = self.model.type2(current_era, era_List[-1]) lives += increment if lives <= 0: self.previous_era = era_List[-1] print "Early termination" break era_List.append(current_era) current_era = [] n += 1 total += x.energy return total, n, output def extrapolate(self, frontier, one, f, cf): out = Thing(id=one.id, have=list(one.have), score=self.model.energy(one.have), energy=self.model.energy(one.have)) two, three, four = self.threeOthers(frontier, one) changed = False for d in self.model.decisions(): x, y, z = two.have[d], three.have[d], four.have[d] if random() < cf: changed = True new = x + f * (y - z) out.have[d] = max(0.0, min(new, 1.0)) # keep in range if not changed: d = self.a(self.model.decisions()) out.have[d] = two.have[d] out.score = self.model.energy(out.have) # remember to score it out.energy = self.model.energy(out.have) return out
class DE(): def __init__(self, model = "DTLZ7"): self.top_bound = [3.6, 0.8, 28.0, 8.3, 8.3, 3.9, 5.5] self.bottom_bound = [2.6, 0.7, 17.0, 7.3, 7.3, 2.9, 5] self.f2_high = -10**6 self.f2_low = 10**6 self.f1_high = -10**6 self.f1_low = 10**6 self.evals = 0 self.median = [] if model == "Osyczka": self.model = Osyczka() elif model == "Golinski": self.model = Golinski() elif model == "Kursawe": self.model = Kursawe() elif model == "Schaffer": self.model = Schaffer() elif model == "DTLZ7": self.model = DTLZ7(10, 2) self.best_solution = Thing() self.best_solution.score = 0; self.best_solution.energy = 1; self.best_solution.have = [] self.previous_era = [] #Returns three different things that are not 'avoid'. def threeOthers(self, lst, avoid): def oneOther(): x = self.a(lst) while x in seen: x = self.a(lst) seen.append( x ) return x # ----------------------- seen = list(avoid.have) this = oneOther() that = oneOther() theOtherThing = oneOther() return this, that, theOtherThing def a(self, lst): return lst[self.n(len(lst))] def n(self, number): return int(uniform(0, number)) def candidate(self): # something = [uniform(self.model.low(d), self.model.high(d)) # for d in self.model.decisions()] something = self.model.get_random_state() new = Thing() new.have = something new.score = self.model.energy(new.have) new.energy = self.model.energy(new.have) return new def run(self, maximum = 100, # number of repeats np = 100, # number of candidates f = 0.75, # extrapolate amount cf = 0.3, # prob of cross-over epsilon = 0.01, s = 0.1 ): print "asd" + str(np) frontier = [self.candidate() for _ in range(np)] print "test" median = [] print maximum for k in range(0, 100): total, n, output = self.update(f, cf, frontier) self.evals += n frontier.sort(key=lambda x: x.energy) #print output + "Frontier energy:" + str(total) + " Count:" + str(n) + " Max Energy:" + str(frontier[0].energy) + " Min Energy:" + str(frontier[len(frontier)-1].energy) min = frontier[0].energy max = frontier[len(frontier)-1].energy median.append(frontier[int(len(frontier)/2)].score) big = max - (max-min)*s/100 new_frontier = [x for x in frontier if x.energy <= big] frontier = new_frontier if (n > 0 and total/n > (1 - epsilon)) or n <= 0 or len(frontier) < 3: break print "Median values:" print median return self.previous_era def update(self, f, cf, frontier, total = 0.0, n = 0): output = "" MAX_LIVES = 10 ERA_LENGTH = 10 era_List = [] current_era = [] lives = MAX_LIVES for x in frontier: s = x.energy new = self.extrapolate(frontier, x, f, cf) # if self.model.type1(x.have, new.have): # output += "." if s < new.energy: output += "." # elif self.model.type1(new.have, x.have): # x.energy = new.energy # x.score = new.score # x.have = new.have # output += "+" elif new.energy < s: x.energy = new.energy x.score = new.score x.have = new.have output += "+" # if self.model.type1(new.have, self.best_solution.have): # self.best_solution.score = new.score # self.best_solution.have = new.have # self.best_solution.energy = new.energy # self.best_solution.evals = self.evals + n # output += "!" if new.energy < self.best_solution.energy: self.best_solution.score = new.score self.best_solution.have = new.have self.best_solution.energy = new.energy self.best_solution.evals = self.evals + n output += "!" if len(output) == 50: print output + " Best Solution: [", for a in self.best_solution.have: print("%.2f " % a), print "] Energy: " + str(self.best_solution.score) + " Evals: " + str(self.evals + n) current_era.append(x.have) if len(current_era) == ERA_LENGTH and len(era_List) > 0: increment = self.model.type2(current_era, era_List[-1]) lives += increment if lives <= 0: self.previous_era = era_List[-1] print "Early termination" break era_List.append(current_era) current_era = [] n += 1 total += x.energy return total, n, output def extrapolate(self, frontier, one, f, cf): out = Thing(id = one.id, have = list(one.have), score = self.model.energy(one.have), energy = self.model.energy(one.have)) two, three, four = self.threeOthers(frontier, one) changed = False for d in self.model.decisions(): x, y, z = two.have[d], three.have[d], four.have[d] if random() < cf: changed = True new = x + f*(y - z) out.have[d] = max(0.0, min(new, 1.0)) # keep in range if not changed: d = self.a(self.model.decisions()) out.have[d] = two.have[d] out.score = self.model.energy(out.have) # remember to score it out.energy = self.model.energy(out.have) return out
class DE(): def __init__(self, model = "Osyczka"): self.top_bound = [3.6, 0.8, 28.0, 8.3, 8.3, 3.9, 5.5] self.bottom_bound = [2.6, 0.7, 17.0, 7.3, 7.3, 2.9, 5] self.f2_high = -10**6 self.f2_low = 10**6 self.f1_high = -10**6 self.f1_low = 10**6 self.evals = 0 self.median = [] if model == "Osyczka": self.model = Osyczka() elif model == "Golinski": self.model = Golinski() elif model == "Kursawe": self.model = Kursawe() elif model == "Schaffer": self.model = Schaffer() self.best_solution = Thing() self.best_solution.score = 0; self.best_solution.energy = 1; self.best_solution.have = [] #Returns three different things that are not 'avoid'. def threeOthers(self, lst, avoid): def oneOther(): x = self.a(lst) while x in seen: x = self.a(lst) seen.append( x ) return x # ----------------------- seen = list(avoid.have) this = oneOther() that = oneOther() theOtherThing = oneOther() return this, that, theOtherThing def a(self, lst): return lst[self.n(len(lst))] def n(self, number): return int(uniform(0, number)) def candidate(self): something = [uniform(self.model.low(d), self.model.high(d)) for d in self.model.decisions()] new = Thing() new.have = something new.score = self.model.energy(new.have) new.energy = self.model.aggregate_energy(new.have) return new def run(self, max = 100, # number of repeats np = 100, # number of candidates f = 0.75, # extrapolate amount cf = 0.3, # prob of cross-over epsilon = 0.01, s = 0.1 ): frontier = [self.candidate() for _ in range(np)] median = [] for k in range(max): total, n, output = self.update(f, cf, frontier) self.evals += n frontier.sort(key=lambda x: x.energy) #print output + "Frontier energy:" + str(total) + " Count:" + str(n) + " Max Energy:" + str(frontier[0].energy) + " Min Energy:" + str(frontier[len(frontier)-1].energy) min = frontier[0].energy max = frontier[len(frontier)-1].energy median.append(frontier[int(len(frontier)/2)].energy) big = max - (max-min)*s/100 new_frontier = [x for x in frontier if x.energy <= big] frontier = new_frontier if (n > 0 and total/n > (1 - epsilon)) or n <= 0 or len(frontier) < 3: break # print "Median values:" # print median return self.best_solution.have def update(self, f, cf, frontier, total = 0.0, n = 0): output = "" for x in frontier: s = x.energy new = self.extrapolate(frontier, x, f, cf) if s < new.energy: output += "." elif new.energy < s: x.energy = new.energy x.score = new.score x.have = new.have output += "+" if new.energy < self.best_solution.energy: self.best_solution.score = new.score self.best_solution.have = new.have self.best_solution.energy = new.energy self.best_solution.evals = self.evals + n output += "!" if len(output) == 50: print output + " Best Solution: [", for a in self.best_solution.have: print("%.2f " % a), print "] Energy: " + str(self.best_solution.score) + " Evals: " + str(self.evals + n) n += 1 total += x.energy return total, n, output def extrapolate(self, frontier, one, f, cf): out = Thing(id = one.id, have = list(one.have), score = self.model.energy(one.have), energy = self.model.aggregate_energy(one.have)) two, three, four = self.threeOthers(frontier, one) changed = False for d in self.model.decisions(): x, y, z = two.have[d], three.have[d], four.have[d] if random() < cf: changed = True new = x + f*(y - z) out.have[d] = self.model.trim(new, d) # keep in range if not changed: d = self.a(self.model.decisions()) out.have[d] = two.have[d] out.score = self.model.energy(out.have) # remember to score it out.energy = self.model.aggregate_energy(out.have) return out
class DE(): def __init__(self, model="Osyczka"): self.top_bound = [3.6, 0.8, 28.0, 8.3, 8.3, 3.9, 5.5] self.bottom_bound = [2.6, 0.7, 17.0, 7.3, 7.3, 2.9, 5] self.f2_high = -10**6 self.f2_low = 10**6 self.f1_high = -10**6 self.f1_low = 10**6 self.evals = 0 self.median = [] if model == "Osyczka": self.model = Osyczka() elif model == "Golinski": self.model = Golinski() elif model == "Kursawe": self.model = Kursawe() elif model == "Schaffer": self.model = Schaffer() self.best_solution = Thing() self.best_solution.score = 0 self.best_solution.energy = 1 self.best_solution.have = [] #Returns three different things that are not 'avoid'. def threeOthers(self, lst, avoid): def oneOther(): x = self.a(lst) while x in seen: x = self.a(lst) seen.append(x) return x # ----------------------- seen = list(avoid.have) this = oneOther() that = oneOther() theOtherThing = oneOther() return this, that, theOtherThing def a(self, lst): return lst[self.n(len(lst))] def n(self, number): return int(uniform(0, number)) def candidate(self): something = [ uniform(self.model.low(d), self.model.high(d)) for d in self.model.decisions() ] new = Thing() new.have = something new.score = self.model.energy(new.have) new.energy = self.model.aggregate_energy(new.have) return new def run( self, max=100, # number of repeats np=100, # number of candidates f=0.75, # extrapolate amount cf=0.3, # prob of cross-over epsilon=0.01, s=0.1): frontier = [self.candidate() for _ in range(np)] median = [] for k in range(max): total, n, output = self.update(f, cf, frontier) self.evals += n frontier.sort(key=lambda x: x.energy) #print output + "Frontier energy:" + str(total) + " Count:" + str(n) + " Max Energy:" + str(frontier[0].energy) + " Min Energy:" + str(frontier[len(frontier)-1].energy) min = frontier[0].energy max = frontier[len(frontier) - 1].energy median.append(frontier[int(len(frontier) / 2)].energy) big = max - (max - min) * s / 100 new_frontier = [x for x in frontier if x.energy <= big] frontier = new_frontier if (n > 0 and total / n > (1 - epsilon)) or n <= 0 or len(frontier) < 3: break # print "Median values:" # print median return self.best_solution.have def update(self, f, cf, frontier, total=0.0, n=0): output = "" for x in frontier: s = x.energy new = self.extrapolate(frontier, x, f, cf) if s < new.energy: output += "." elif new.energy < s: x.energy = new.energy x.score = new.score x.have = new.have output += "+" if new.energy < self.best_solution.energy: self.best_solution.score = new.score self.best_solution.have = new.have self.best_solution.energy = new.energy self.best_solution.evals = self.evals + n output += "!" if len(output) == 50: print output + " Best Solution: [", for a in self.best_solution.have: print("%.2f " % a), print "] Energy: " + str( self.best_solution.score) + " Evals: " + str(self.evals + n) n += 1 total += x.energy return total, n, output def extrapolate(self, frontier, one, f, cf): out = Thing(id=one.id, have=list(one.have), score=self.model.energy(one.have), energy=self.model.aggregate_energy(one.have)) two, three, four = self.threeOthers(frontier, one) changed = False for d in self.model.decisions(): x, y, z = two.have[d], three.have[d], four.have[d] if random() < cf: changed = True new = x + f * (y - z) out.have[d] = self.model.trim(new, d) # keep in range if not changed: d = self.a(self.model.decisions()) out.have[d] = two.have[d] out.score = self.model.energy(out.have) # remember to score it out.energy = self.model.aggregate_energy(out.have) return out