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+# **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/)
+![Python](https://img.shields.io/badge/python->=3.5-green.svg)
+![Pypi](https://img.shields.io/badge/pypi-2.5.1-blue.svg)
+[![Download](https://img.shields.io/pypi/dm/geatpy.svg)](https://pypi.python.org/pypi/geatpy)
+[![License](https://img.shields.io/pypi/l/geatpy.svg)](https://github.com/geatpy-dev/geatpy/blob/master/LICENSE)
+
+## 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/
+
+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 2.5.1
+
+* Support setting random seed in Geatpy.
+
+* The kernel of Geatpy is more stable.
+
+* Support 'xovud' in 'xovpmx'.
+
+* A better EA framework with higher performance.
+
+* Add MOEA/D-DE algorithm template.
+
+## 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, 3.7 or 3.8 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 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__':
+    """=========================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.getReferObjV() # 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 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 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.
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