This repository contains code and documents that demonstrate the essence of genetic algorithms (GAs) and how they can be used for optimization problems.
We focus on single objective optimization here. For multiobjective optimization using GAs, please see another repo called nsga-ii at https://github.com/ncxiao/nsga-ii, or this notebook.
genetic algorithms.ipynbintroduction to genetic algorithms and how to implement them using Python.GA Class.ipynba Python class calledGAand how to use it for solving specific numerical and spatial optimization problems.
The following is a list of the source code:
README.mdthis filega.pya Python module forGAandParametersclassesga_base_binary.pyderiving thebGAclass for a numerical problem using binary stringsga_base_pmed.pyderiving thebGAclass for a p-median problemga_binary.pyGA using binary strings (without using theGAclass)ga_pmed.pyGA for p-median problems (without using theGAclass)
From terminal:
$ python3 ga_base_binary.py
(50, 44.9)
[50, 49, 50, 49, 49, 50, 50, 50, 48, 50]
$ python3 ga_base_pmed.py
(40, 47.75)
mutation obj
0 38
0.1 72
0.2 79
0.3 86
0.4 91
0.5 84
0.6 90
0.7 85
0.8 87
0.9 78
1 80
From Python shell:
>>> from ga_base_pmed import *
>>> owndata = {
... 'n': 8,
... 'distmatrix': [
... [0, 3, 13, 5, 12, 16, 17, 20],
... [3, 0, 10, 8, 9, 13, 14, 23],
... [13, 10, 0, 8, 9, 3, 14, 15],
... [5, 8, 8, 0, 17, 11, 22, 15],
... [12, 9, 9, 17, 0, 6, 5, 16],
... [16, 13, 3, 11, 6, 0, 11, 12],
... [17, 14, 14, 22, 5, 11, 0, 11],
... [20, 23, 15, 15, 16, 12, 11, 0]],
... 'p': 2
... }
>>>
>>> params = Parameters(
... popsize = 4,
... numgen = 10,
... pcrossover = 0.9,
... pmutation = 0.1,
... elitism = False,
... minmax = 'min')
>>> myga = GAPMed(params, owndata)
>>> res = myga.run()
>>> print(res)
(40, 42.75)
>>> from ga_base_binary import *
>>> params = Parameters(
... popsize = 10,
... numgen = 10,
... pcrossover = 0.9,
... pmutation = 0.1,
... elitism = True)
>>>
>>> myga = GABin(params)
>>> res = myga.run()
>>> print(res)
(50, 38.4)
>>> print([myga.run()[0] for _ in range(10)])
[49, 48, 50, 49, 49, 50, 50, 49, 50, 50]
Ningchuan Xiao ([email protected])
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent