# geatpy

**Repository Path**: wangcd0710/geatpy

## Basic Information

- **Project Name**: geatpy
- **Description**: geatpy库克隆包
- **Primary Language**: Unknown
- **License**: LGPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 3
- **Created**: 2022-05-27
- **Last Updated**: 2023-03-01

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

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

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## Introduction
* **Website (including documentation)**: http://www.geatpy.com
* **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 <filename>.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.

![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 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:

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

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:

![image](https://github.com/geatpy-dev/geatpy/blob/master/testbed/soea_test/result/Trace%20Plot.svg)

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.