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import random as rnd |
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import copy |
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from NiaPy.benchmarks.utility import Utility |
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__all__ = ['GeneticAlgorithm'] |
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View Code Duplication |
class Chromosome(object): |
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def __init__(self, D, LB, UB): |
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self.D = D |
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self.LB = LB |
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self.UB = UB |
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self.Solution = [] |
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self.Fitness = float('inf') |
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self.generateSolution() |
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def generateSolution(self): |
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self.Solution = [self.LB + (self.UB - self.LB) * rnd.random() |
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for _i in range(self.D)] |
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def evaluate(self): |
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self.Fitness = Chromosome.FuncEval(self.D, self.Solution) |
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def repair(self): |
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for i in range(self.D): |
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if self.Solution[i] > self.UB: |
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self.Solution[i] = self.UB |
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if self.Solution[i] < self.LB: |
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self.Solution[i] = self.LB |
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def __eq__(self, other): |
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return self.Solution == other.Solution and self.Fitness == other.Fitness |
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def toString(self): |
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print([i for i in self.Solution]) |
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class GeneticAlgorithm(object): |
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r"""Implementation of Genetic algorithm. |
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**Algorithm:** Genetic algorithm |
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**Date:** 2018 |
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**Author:** Uros Mlakar |
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**License:** MIT |
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""" |
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def __init__(self, D, NP, nFES, Ts, Mr, gamma, benchmark): |
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r"""**__init__(self, D, NP, nFES, Ts, Mr, gamma, benchmark)**. |
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Arguments: |
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D {integer} -- dimension of problem |
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NP {integer} -- population size |
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nFES {integer} -- number of function evaluations |
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Ts {decimal} -- tournament selection |
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Mr {decimal} -- mutation rate |
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gamma {decimal} -- minimum frequency |
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benchmark {object} -- benchmark implementation object |
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Raises: |
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TypeError -- Raised when given benchmark function which does not exists. |
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""" |
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self.benchmark = Utility().get_benchmark(benchmark) |
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self.NP = NP |
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self.D = D |
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self.Ts = Ts |
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self.Mr = Mr |
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self.gamma = gamma |
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self.Lower = self.benchmark.Lower |
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self.Upper = self.benchmark.Upper |
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self.Population = [] |
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self.nFES = nFES |
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self.FEs = 0 |
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self.Done = False |
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Chromosome.FuncEval = staticmethod(self.benchmark.function()) |
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self.Best = Chromosome(self.D, self.Lower, self.Upper) |
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def checkForBest(self, pChromosome): |
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if pChromosome.Fitness <= self.Best.Fitness: |
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self.Best = copy.deepcopy(pChromosome) |
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def TournamentSelection(self): |
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indices = list(range(self.NP)) |
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rnd.shuffle(indices) |
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tPop = [] |
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for i in range(self.Ts): |
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tPop.append(self.Population[i]) |
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tPop.sort(key=lambda x: x.Fitness) |
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self.Population.remove(tPop[0]) |
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self.Population.remove(tPop[1]) |
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return tPop[0], tPop[1] |
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def CrossOver(self, parent1, parent2): |
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alpha = [-self.gamma + (1 + 2 * self.gamma) * rnd.random() |
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for i in range(self.D)] |
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child1 = Chromosome(self.D, self.Lower, self.Upper) |
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child2 = Chromosome(self.D, self.Lower, self.Upper) |
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child1.Solution = [alpha[i] * parent1.Solution[i] + |
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(1 - alpha[i]) * parent2.Solution[i] for i in range(self.D)] |
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child2.Solution = [alpha[i] * parent2.Solution[i] + |
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(1 - alpha[i]) * parent1.Solution[i] for i in range(self.D)] |
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return child1, child2 |
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def Mutate(self, child): |
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for i in range(self.D): |
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if rnd.random() < self.Mr: |
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sigma = 0.20 * float(child.UB - child.LB) |
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child.Solution[i] = min( |
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max(rnd.gauss(child.Solution[i], sigma), child.LB), child.UB) |
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def init(self): |
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for i in range(self.NP): |
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self.Population.append(Chromosome(self.D, self.Lower, self.Upper)) |
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self.Population[i].evaluate() |
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self.checkForBest(self.Population[i]) |
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def tryEval(self, c): |
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if self.FEs < self.nFES: |
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self.FEs += 1 |
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c.evaluate() |
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else: |
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self.Done = True |
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def run(self): |
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self.init() |
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self.FEs = self.NP |
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while not self.Done: |
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for _k in range(int(self.NP / 2)): |
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parent1, parent2 = self.TournamentSelection() |
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child1, child2 = self.CrossOver(parent1, parent2) |
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self.Mutate(child1) |
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self.Mutate(child2) |
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child1.repair() |
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child2.repair() |
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self.tryEval(child1) |
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self.tryEval(child2) |
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tPop = [parent1, parent2, child1, child2] |
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tPop.sort(key=lambda x: x.Fitness) |
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self.Population.append(tPop[0]) |
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self.Population.append(tPop[1]) |
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for i in range(self.NP): |
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self.checkForBest(self.Population[i]) |
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return self.Best.Fitness |
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NiaPy
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