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jazzbin edited this page Aug 27, 2018 · 30 revisions

Geatpy

The Genetic and Evolutionary Algorithm Toolbox for Python

Travis Python Pypi

Introduction

It provides:

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

  • 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.Via pip::

pip install geatpy

2.From source::

python setup.py install

or

pip install <filename>.whl

Attention: Geatpy requires numpy>=1.10.0 and matplotlib>=1.5.1, 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 Python3.5, 3.6 or 3.7 in x32 or x64 in Windows systems.

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 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. To get some examples, please link to http://www.geatpy.com/tutorial.

  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

"""==================================variables setting================================"""
ranges = np.vstack([np.zeros((1,6)), np.ones((1,6))]) # define the ranges of variables in DTLZ1
borders = np.vstack([np.ones((1,6)), np.ones((1,6))]) # define the borders of variables in DTLZ1
precisions = [4] * 30 # define the precision of variables in DTLZ1
FieldDR = ga.crtfld(ranges, borders, precisions) # create the FieldDR
"""=======================use sga2_templet to find the Pareto front==================="""
[ObjV, NDSet, NDSetObjV, times] = ga.nsga2_templet(AIM_M, 'aimfuc',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, drawing = 1)

The partial of the result is:

image

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

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

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