# geatpy

**Repository Path**: rigarch/geatpy

## Basic Information

- **Project Name**: geatpy
- **Description**: Evolutionary algorithm toolbox and framework with high performance for Python
- **Primary Language**: Unknown
- **License**: LGPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-01-18
- **Last Updated**: 2022-01-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# **Geatpy2** 
The Genetic and Evolutionary Algorithm Toolbox for Python with high performance.

![Travis](https://travis-ci.org/geatpy-dev/geatpy.svg?branch=master)
[![Package Status](https://img.shields.io/pypi/status/geatpy.svg)](https://pypi.org/project/geatpy/)
[![License](https://img.shields.io/pypi/l/geatpy.svg)](https://github.com/geatpy-dev/geatpy/blob/master/LICENSE)
![Python](https://img.shields.io/badge/python->=3.5-green.svg)
![Pypi](https://img.shields.io/badge/pypi-2.2.3-blue.svg)

## Introduction
* **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:

* global optimization capabilities in **Python** using genetic and other evolutionary algorithms to solve problems unsuitable for traditional optimization approaches.

* a great many of **evolutionary operators**, so that you can deal with **single, multiple and many objective optimization** problems.

## Improvement of Geatpy 2.2.3

* Improve the performance of crtpp and ranking.

* Rebuild the core of NSGA-II, NSGA-III and RVEA to get higher performances.

* Add new multi-objective optimization test problem: TNK.

## Installation
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.

## Versions

**Geatpy** must run under **Python**3.5, 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

Quick start
-----------

Here is the UML figure of Geatpy2.

![image](https://github.com/geatpy-dev/geatpy/blob/master/structure.png)

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 calBest(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
"""=========================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:

![image](https://github.com/geatpy-dev/geatpy/blob/master/geatpy/testbed/moea_test/moea_test_DTLZ/Pareto%20Front.svg)

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".

```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 calBest(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
"""=========================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:

![image](https://github.com/geatpy-dev/geatpy/blob/master/geatpy/testbed/soea_test/soea_test_Ackley/result1.svg)

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.