Skip to content

Latest commit

 

History

History
125 lines (71 loc) · 4.41 KB

README.rst

File metadata and controls

125 lines (71 loc) · 4.41 KB
Raw

Geatpy

The Genetic and Evolutionary Algorithm Toolbox for Python

Build Status platform versions

Introduction

  1. Website (including documentation): https://www.geatpy.com (almost ready)
  2. Contact us: https://www.geatpy.com/supports
  3. Source: https://github.com/geatpy-dev/geatpy
  4. Bug reports: https://github.com/geatpy-dev/geatpy/issues
  5. Franchised blog https://blog.csdn.net/qq_33353186

It provides:

  1. global optimization capabilities in Python using genetic and evolutionary algorithm to solve problems unsuitable for traditional optimization approaches.
  2. 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.

Installation

  1. From pip:

    pip install geatpy

  2. From source:

    python setup.py install

Attention: Geatpy requires numpy>=1.10.0 and matplotlib>=1.5.1, the installation program will help you install all the requires. But if something wrong happened, you have to install all requires by yourselves.

Versions

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

Quick start

You can use Geatpy to solve single or multi-objective optimization problems mainly in two ways:

  1. 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.
  2. 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 aimfuc(Chrom, M = 3): # M is the dimensions of DTLZ1.

x = Chrom.T # Chrom is a numpy array standing for the chromosomes of a 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 # 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 DTLZ1

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.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 = False, drawing = 2)

The partial of the result can be seen in:

https://github.com/geatpy-dev/geatpy/blob/master/geatpy/demo/DTLZ_demo/Pareto%20Front.png

To get more examples and more tutorials, please link to http://www.geatpy.com/tutorial.

There are also some demos in Geatpy's source. Including ZDT1/2/3/4/6、 DTLZ1/2/3/4、single-objective examples、discrete problem solving and so forth.