The Genetic and Evolutionary Algorithm Toolbox for Python
- Website (including documentation): http://www.geatpy.com
- Tutorial pdf: https://github.com/geatpy-dev/geatpy/tree/master/geatpy/doc/tutorial
- Demo : https://github.com/geatpy-dev/geatpy/tree/master/geatpy/demo
- Pypi page : https://pypi.org/project/geatpy/
- Contact us: http://www.geatpy.com/support
- Source code: https://github.com/geatpy-dev/geatpy/tree/master/geatpy/source-code
- Bug reports: https://github.com/geatpy-dev/geatpy/issues
- Franchised blog: https://blog.csdn.net/qq_33353186
It provides:
-
global optimization capabilities in Python using genetic and evolutionary algorithm to solve problems unsuitable for traditional optimization approaches.
-
a great many of genetic and evolutionary operators, so that you can deal with single or multi-objective optimization problems.
It can work faster with numpy+mkl. If you want to speed your projects, please install numpy+mkl.
1.Via pip:
pip install geatpy
2.From source:
python setup.py install
or
pip install <filename>.whl
Attention: Geatpy requires numpy>=1.12.1 and matplotlib>=2.0.0, 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 x32 or x64 in Windows systems.
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 --user --upgrade geatpy
You can use Geatpy mainly in two ways:
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Create a script, write all the codes on it and run. It's the easiest way, but it needs much too codes and is not good for reuse. To get some examples, please link to http://www.geatpy.com/tutorials.
-
Using templets and functional interfaces. For example, we try to find the pareto front of DTLZ1, do as the following:
2.1) Write DTLZ1 function on a file named "aimfuc.py" as a functional interfaces:
"""aimfuc.py"""
# DTLZ1
def DTLZ1(Chrom, LegV): # LegV is legal-sign of the population
M = 3 # M is the dimensions of DTLZ1
x = Chrom.T # Chrom is a numpy array standing for the chromosomes of the population
XM = x[M-1:]
k = x.shape[0] - M + 1
gx = 100 * (k + np.sum((XM - 0.5) ** 2 - np.cos(20 * np.pi * (XM - 0.5)), 0))
ObjV = (np.array([[]]).T) * np.zeros((1, Chrom.shape[0])) # define ObjV to recod function values
ObjV = np.vstack([ObjV, 0.5 * np.cumprod(x[:M-1], 0)[-1] * (1 + gx)])
for i in range(2, M):
ObjV = np.vstack([ObjV, 0.5 * np.cumprod(x[: M-i], 0)[-1] * (1 - x[M-i]) * (1 + gx)])
ObjV = np.vstack([ObjV, 0.5 * (1 - x[0]) * (1 + gx)])
return [ObjV.T, LegV] # use '.T' to change ObjV so that each row stands for function values of each individual of the population
2.2) Write the main script using NSGA-II templet of Geatpy to solve the problem.
"""main.py"""
import numpy as np
import geatpy as ga # import geatpy
AIM_M = __import__('aimfuc') # get the address of objective function
AIM_F = 'DTLZ1' # You can set DTL1,2,3 or 4
"""==================================variables setting================================"""
ranges = np.vstack([np.zeros((1,7)), np.ones((1,7))]) # define the ranges of variables in DTLZ1
borders = np.vstack([np.ones((1,7)), np.ones((1,7))]) # define the borders of variables in DTLZ1
FieldDR = ga.crtfld(ranges, borders) # create the FieldDR
"""=======================use sga2_templet to find the Pareto front==================="""
[ObjV, NDSet, NDSetObjV, times] = ga.moea_nsga2_templet(AIM_M, AIM_F, None, None, FieldDR, problem = 'R', maxormin = 1, MAXGEN = 1000, MAXSIZE = 2000, NIND = 50, SUBPOP = 1, GGAP = 1, selectStyle = 'tour', recombinStyle = 'xovdprs', recopt = 0.9, pm = None, distribute = True, drawing = 1)
The partial of the pareto front is:
To get more tutorials, please link to http://www.geatpy.com
There are more demos in Geatpy's source. Including ZDT1/2/3/4/6、 DTLZ1/2/3/4、single-objective examples、discrete problem solving and so forth.
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