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https://github.com/geatpy-dev/geatpy
Evolutionary algorithm toolbox and framework with high performance for Python
https://github.com/geatpy-dev/geatpy
de es evolutionary-algorithms ga geatpy high-performance moead nsga parallel-computing rvea
Last synced: 6 days ago
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Evolutionary algorithm toolbox and framework with high performance for Python
- Host: GitHub
- URL: https://github.com/geatpy-dev/geatpy
- Owner: geatpy-dev
- License: lgpl-3.0
- Created: 2018-08-23T13:26:31.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-09-20T10:00:21.000Z (4 months ago)
- Last Synced: 2024-10-29T15:38:45.733Z (3 months ago)
- Topics: de, es, evolutionary-algorithms, ga, geatpy, high-performance, moead, nsga, parallel-computing, rvea
- Language: Python
- Homepage: http://www.geatpy.com
- Size: 575 MB
- Stars: 2,006
- Watchers: 46
- Forks: 725
- Open Issues: 156
-
Metadata Files:
- Readme: README.md
- Changelog: history.txt
- License: LICENSE
Awesome Lists containing this project
- StarryDivineSky - geatpy-dev/geatpy
README
# **Geatpy2**
The Genetic and Evolutionary Algorithm Toolbox for Python with high performance.
[](https://pypi.org/project/geatpy/)
[](https://pypi.python.org/pypi/geatpy)
[](https://github.com/geatpy-dev/geatpy/blob/master/LICENSE)
[](https://gitter.im/geatpy2/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
## Introduction
* **Website (including documentation)**: https://geatpy.github.io/
* **Quickstart**: https://github.com/geatpy-dev/geatpy/blob/master/docs/quickstart.html
* **Demo** : https://github.com/geatpy-dev/geatpy/tree/master/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/
The features of Geatpy:
* Capability of solving single-objective, multi-objectives, many-objectives and combinatorial optimization problems fast.
* A huge number of operators with high performance of evolutionary algorithms (selection, recombination, mutation, migration...).
* Support numerous encodings for the chromosome of the population.
* Many evolutionary algorithm templates, including GA, DE, ES for single/multi-objective(s) evolution.
* Multiple population evolution.
* Support polysomy evolution.
* Parallelization and distribution of evaluations.
* Testbeds containing most common benchmarks functions.
* Support tracking analysis of the evolution iteration.
* Many evaluation metrics of algorithms.
## Improvement of Geatpy 2.7.0
* Add a new way to define the aim function of the problem.
* Support calculating objectives and constraints for the variables of only one individual.
* Add a optimize function to do the optimization more convenient.
* Add new open-source plotting functions.
* Remove the dependency on scipy.
* A new and faster core.
## Installation
1.Installing online:
pip install geatpy
2.From source:
python setup.py install
or
pip install .whl
**Attention**: Geatpy requires numpy>=1.17.0 and matplotlib>=3.0.0, the installation program won't help you install them so that you have to install both of them by yourselves.
## Versions
**Geatpy** must run under **Python**3.5, 3.6, 3.7, 3.8, 3.9, or 3.10 in Windows x32/x64, Linux x64 or MacOS 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
Quick start
-----------
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:
```python
"""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 calReferObjV(self): # Calculate the theoretic global optimal solution here.
uniformPoint, ans = ea.crtup(self.M, 10000) # create 10000 uniform points.
realBestObjV = uniformPoint / 2
return realBestObjV
```
2.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:
```python
"""main.py"""
import geatpy as ea # Import geatpy
from MyProblem import MyProblem # Import MyProblem class
if __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 optimization
res = ea.optimize(algorithm, verbose=False, drawing=1, outputMsg=True, drawLog=True, saveFlag=True)
```
Run the "main.py" and the part of the result is:
Execution time: 0.3650233745574951 s
Evaluation number: 45500
The number of non-dominated solutions is: 91
gd: 0.00033
igd: 0.02084
hv: 0.84061
spacing: 0.00158
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".
```python
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 calReferObjV(self): # Calculate the global optimal solution here.
realBestObjV = np.array([[0]])
return realBestObjV
```
2.Write "main.py" to execute the algorithm templet to solve the problem.
```python
import geatpy as ea # import geatpy
import numpy as np
from MyProblem import Ackley
if __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 DE
algorithm.recOper.XOVR = 0.2 # Set the Cr of DE (Here it is marked as XOVR)
# Do the optimization
res = ea.optimize(algorithm, verbose=False, drawing=1, outputMsg=True, drawLog=True, saveFlag=True, dirName='result')
```
Part of the result is:
Execution time: 0.256328821182251 s
Evaluation number: 20000
The best objective value is: 3.209895993450118e-08
To get more tutorials, please link to http://www.geatpy.com.