Introduction

How to Cite

Improvement of Geatpy 2.7.0

Installation

pip install geatpy

python setup.py install

pip install <filename>.whl

Versions

pip install --upgrade geatpy

pip install --upgrade --user geatpy

Quick start

"""MyProblem.py"""importnumpyasnpimportgeatpyaseaclassMyProblem(ea.Problem):# Inherited from Problem class.def__init__(self,M):# M is the number of objects.name='DTLZ1'# Problem's name.maxormins= [1]*M# All objects are need to be minimized.Dim=M+4# Set the dimension of decision variables.varTypes= [0]*Dim# Set the types of decision variables. 0 means continuous while 1 means discrete.lb= [0]*Dim# The lower bound of each decision variable.ub= [1]*Dim# The upper bound of each decision variable.lbin= [1]*Dim# Whether the lower boundary is included.ubin= [1]*Dim# Whether the upper boundary is included.# Call the superclass's constructor to complete the instantiationea.Problem.__init__(self,name,M,maxormins,Dim,varTypes,lb,ub,lbin,ubin)defaimFunc(self,pop):# Write the aim function here, pop is an object of Population class.Vars=pop.Phen# Get the decision variablesXM=Vars[:,(self.M-1):]g=np.array([100* (self.Dim-self.M+1+np.sum(((XM-0.5)**2-np.cos(20*np.pi* (XM-0.5))),1))]).Tones_metrix=np.ones((Vars.shape[0],1))pop.ObjV=0.5*np.fliplr(np.cumprod(np.hstack([ones_metrix,Vars[:,:self.M-1]]),1))*np.hstack([ones_metrix,1-Vars[:,range(self.M-2,-1,-1)]])*np.tile(1+g, (1,self.M))defcalReferObjV(self):# Calculate the theoretic global optimal solution here.uniformPoint,ans=ea.crtup(self.M,10000)# create 10000 uniform points.realBestObjV=uniformPoint/2returnrealBestObjV

"""main.py"""importgeatpyasea# Import geatpyfromMyProblemimportMyProblem# Import MyProblem classif__name__=='__main__':M=3# Set the number of objects.problem=MyProblem(M)# Instantiate MyProblem class# Instantiate a algorithm class.algorithm=ea.moea_NSGA3_templet(problem,ea.Population(Encoding='RI',NIND=100),# Set 100 individuals.MAXGEN=500,# Set the max iteration number.logTras=1)# Set the frequency of logging. If it is zero, it would not log.# Do the optimizationres=ea.optimize(algorithm,verbose=False,drawing=1,outputMsg=True,drawLog=True,saveFlag=True)

importnumpyasnpimportgeatpyaseaclassAckley(ea.Problem):# Inherited from Problem class.def__init__(self,D=30):name='Ackley'# Problem's name.M=1# Set the number of objects.maxormins= [1]*M# All objects are need to be minimized.Dim=D# Set the dimension of decision variables.varTypes= [0]*Dim# Set the types of decision variables. 0 means continuous while 1 means discrete.lb= [-32.768]*Dim# The lower bound of each decision variable.ub= [32.768]*Dim# The upper bound of each decision variable.lbin= [1]*Dim# Whether the lower boundary is included.ubin= [1]*Dim# Whether the upper boundary is included.# Call the superclass's constructor to complete the instantiationea.Problem.__init__(self,name,M,maxormins,Dim,varTypes,lb,ub,lbin,ubin)defaimFunc(self,pop):# Write the aim function here, pop is an object of Population class.x=pop.Phen# Get the decision variablesn=self.Dimf=np.array([-20*np.exp(-0.2*np.sqrt(1/n*np.sum(x**2,1)))-np.exp(1/n*np.sum(np.cos(2*np.pi*x),1))+np.e+20]).Treturnf,CVdefcalReferObjV(self):# Calculate the global optimal solution here.realBestObjV=np.array([[0]])returnrealBestObjV

importgeatpyasea# import geatpyimportnumpyasnpfromMyProblemimportAckleyif__name__=='__main__':# Instantiate MyProblem class.problem=Ackley(30)# Instantiate a algorithm class.algorithm=ea.soea_DE_rand_1_bin_templet(problem,ea.Population(Encoding='RI',NIND=20),MAXGEN=1000,# Set the max times of iteration.logTras=1)# Set the frequency of logging. If it is zero, it would not log.algorithm.mutOper.F=0.5# Set the F of DEalgorithm.recOper.XOVR=0.2# Set the Cr of DE (Here it is marked as XOVR)# Do the optimizationres=ea.optimize(algorithm,verbose=False,drawing=1,outputMsg=True,drawLog=True,saveFlag=True,dirName='result')