The Genetic and Evolutionary Algorithm Toolbox for Python with high performance.
- Website (including documentation): http://www.geatpy.com
- Demo : https://github.com/geatpy-dev/geatpy/tree/master/geatpy/demo
- Pypi page : https://pypi.org/project/geatpy/
- Contact us: http://geatpy.com/index.php/about/
- Bug reports: https://github.com/geatpy-dev/geatpy/issues
- Notice: http://geatpy.com/index.php/notice/
- FAQ: http://geatpy.com/index.php/faq/
Geatpy provides:
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global optimization capabilities in Python using genetic and other evolutionary algorithms to solve problems unsuitable for traditional optimization approaches.
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a great many of evolutionary operators, so that you can deal with single, multiple and many objective optimization problems.
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Improve the performance of crtpp and ranking.
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Rebuild the core of NSGA-II, NSGA-III and RVEA to get higher performances.
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Add new multi-objective optimization test problem: TNK.
1.Installing online:
pip install geatpy
2.From source:
python setup.py install
or
pip install <filename>.whl
Attention: Geatpy requires numpy>=1.16.0, matplotlib>=3.0.0 and scipy>=1.0.0, the installation program won't help you install them so that you have to install both of them by yourselves.
Geatpy must run under Python3.5, 3.6 or 3.7 in Windows x32/x64, Linux x64 or Mac OS x64.
There are different versions for Windows, Linux and Mac, you can download them from http://geatpy.com/
The version of Geatpy on github is the latest version suitable for Python >= 3.5
You can also update Geatpy by executing the command:
pip install --upgrade geatpy
If something wrong happened, such as decoding error about 'utf8' of pip, run this command instead or execute it as an administrator:
pip install --upgrade --user geatpy
Here is the UML figure of Geatpy2.
For solving a multi-objective optimization problem, you can use Geatpy mainly in two steps:
1.Write down the aim function and some relevant settings in a derivative class named MyProblem, which is inherited from Problem class:
"""MyProblem.py"""
import numpy as np
import geatpy as ea
class MyProblem(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 instantiation
ea.Problem.__init__(self, name, M, maxormins, Dim, varTypes, lb, ub, lbin, ubin)
def aimFunc(self, pop): # Write the aim function here, pop is an object of Population class.
Vars = pop.Phen # Get the decision variables
XM = 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))]).T
ones_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))
def calBest(self): # Calculate the theoretic global optimal solution here.
uniformPoint, ans = ea.crtup(self.M, 10000) # create 10000 uniform points.
realBestObjV = uniformPoint / 2
return realBestObjV2.Instantiate MyProblem class and a derivative class inherited from Algorithm class in a Python script file "main.py" then execute it. For example, trying to find the pareto front of DTLZ1, do as the following:
"""main.py"""
import geatpy as ea # Import geatpy
from MyProblem import MyProblem # Import MyProblem class
"""=========================Instantiate your problem=========================="""
M = 3 # Set the number of objects.
problem = MyProblem(M) # Instantiate MyProblem class
"""===============================Population set=============================="""
Encoding = 'RI' # Encoding type.
NIND = 100 # Set the number of individuals.
Field = ea.crtfld(Encoding, problem.varTypes, problem.ranges, problem.borders) # Create the field descriptor.
population = ea.Population(Encoding, Field, NIND) # Instantiate Population class(Just instantiate, not initialize the population yet.)
"""================================Algorithm set==============================="""
myAlgorithm = ea.moea_NSGA3_templet(problem, population) # Instantiate a algorithm class.
myAlgorithm.MAXGEN = 500 # Set the max times of iteration.
"""===============================Start evolution=============================="""
NDSet = myAlgorithm.run() # Run the algorithm templet.
"""=============================Analyze the result============================="""
PF = problem.calBest() # Get the global pareto front.
GD = ea.indicator.GD(NDSet.ObjV, PF) # Calculate GD
IGD = ea.indicator.IGD(NDSet.ObjV, PF) # Calculate IGD
HV = ea.indicator.HV(NDSet.ObjV, PF) # Calculate HV
Space = ea.indicator.spacing(NDSet.ObjV) # Calculate Space
print('The number of non-dominated result: %s'%(NDSet.sizes))
print('GD: ',GD)
print('IGD: ',IGD)
print('HV: ', HV)
print('Space: ', Space)Run the "main.py" and the result is:
The number of non-dominated result: 91
GD: 0.00019492736742063313
IGD: 0.02058320808720775
HV: 0.8413590788841248
Space: 0.00045742613969278813
For solving another problem: Ackley-30D, which has only one object and 30 decision variables, what you need to do is almost the same as above.
1.Write the aim function in "MyProblem.py".
import numpy as np
import geatpy as ea
class Ackley(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 instantiation
ea.Problem.__init__(self, name, M, maxormins, Dim, varTypes, lb, ub, lbin, ubin)
def aimFunc(self, pop): # Write the aim function here, pop is an object of Population class.
x = pop.Phen # Get the decision variables
n = self.Dim
f = 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]).T
return f, CV
def calBest(self): # Calculate the global optimal solution here.
realBestObjV = np.array([[0]])
return realBestObjV2.Write "main.py" to execute the algorithm templet to solve the problem.
import geatpy as ea # import geatpy
import numpy as np
from MyProblem import Ackley
"""=========================Instantiate your problem=========================="""
problem = Ackley(30) # Instantiate MyProblem class.
"""===============================Population set=============================="""
Encoding = 'RI' # Encoding type.
NIND = 20 # Set the number of individuals.
Field = ea.crtfld(Encoding, problem.varTypes, problem.ranges, problem.borders) # Create the field descriptor.
population = ea.Population(Encoding, Field, NIND) # Instantiate Population class(Just instantiate, not initialize the population yet.)
"""================================Algorithm set==============================="""
myAlgorithm = ea.soea_DE_rand_1_bin_templet(problem, population) # Instantiate a algorithm class.
myAlgorithm.MAXGEN = 1000 # Set the max times of iteration.
myAlgorithm.mutOper.F = 0.5 # Set the F of DE
myAlgorithm.recOper.XOVR = 0.2 # Set the Cr of DE (Here it is marked as XOVR)
myAlgorithm.drawing = 1 # 1 means draw the figure of the result
"""===============================Start evolution=============================="""
[population, obj_trace, var_trace] = myAlgorithm.run() # Run the algorithm templet.
"""=============================Analyze the result============================="""
best_gen = np.argmin(obj_trace[:, 1]) # Get the best generation.
best_ObjV = np.min(obj_trace[:, 1])
print('The objective value of the best solution is: %s'%(best_ObjV))
print('Effective iteration times: %s'%(obj_trace.shape[0]))
print('The best generation is: %s'%(best_gen + 1))
print('The number of evolution is: %s'%(myAlgorithm.evalsNum))The result is:
The objective value of the best solution is: 5.8686921988737595e-09
Effective iteration times: 1000
The best generation is: 1000
The number of evolution is: 20000
To get more tutorials, please link to http://www.geatpy.com.