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https://github.com/tusharmakkar08/ga-weather-love
Genetic Algorithm for finding weather preference in Python Using Pyevolve
https://github.com/tusharmakkar08/ga-weather-love
Last synced: 25 days ago
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Genetic Algorithm for finding weather preference in Python Using Pyevolve
- Host: GitHub
- URL: https://github.com/tusharmakkar08/ga-weather-love
- Owner: tusharmakkar08
- Created: 2013-06-05T07:06:42.000Z (over 11 years ago)
- Default Branch: master
- Last Pushed: 2019-07-04T07:31:51.000Z (over 5 years ago)
- Last Synced: 2023-04-01T12:52:12.689Z (over 1 year ago)
- Language: Python
- Homepage:
- Size: 3.91 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
GA-Weather-Love
================
Genetic Algorithm for finding weather preference of a person using Pyevolve .
Requirements :
-------------
* Pyevolve
* Python
Project Details :
----------------
Individuals are the weather descriptions, and weather preference is measured by your fitness function.
* trialGAcode.py :
This is the trial code with extremely simple Fitness Function . Here we have assumed that we like sunny days and hate both rainy and windy days .
SAMPLE OUTPUT FOR TRIAL CODE:
-----------------------------
Gen. 0 (0.00%): Max/Min/Avg Fitness(Raw) [0.23(0.68)/0.18(0.01)/0.19(0.19)]
Gen. 10 (10.00%): Max/Min/Avg Fitness(Raw) [1.17(0.99)/0.00(0.34)/1.16(0.98)]
Gen. 20 (20.00%): Max/Min/Avg Fitness(Raw) [1.17(0.99)/0.00(0.34)/1.16(0.98)]
Gen. 30 (30.00%): Max/Min/Avg Fitness(Raw) [1.15(0.99)/0.00(0.34)/1.09(0.95)]
Gen. 40 (40.00%): Max/Min/Avg Fitness(Raw) [1.17(0.99)/0.00(0.34)/1.16(0.98)]
Gen. 50 (50.00%): Max/Min/Avg Fitness(Raw) [1.17(0.99)/0.00(0.34)/1.16(0.98)]
Gen. 60 (60.00%): Max/Min/Avg Fitness(Raw) [1.16(0.99)/0.00(0.34)/1.14(0.97)]
Gen. 70 (70.00%): Max/Min/Avg Fitness(Raw) [1.16(0.99)/0.00(0.34)/1.14(0.97)]
Gen. 80 (80.00%): Max/Min/Avg Fitness(Raw) [1.17(0.99)/0.00(0.34)/1.16(0.98)]
Gen. 90 (90.00%): Max/Min/Avg Fitness(Raw) [1.17(0.99)/0.00(0.34)/1.16(0.98)]
Gen. 100 (100.00%): Max/Min/Avg Fitness(Raw) [1.15(0.99)/0.00(0.34)/1.09(0.95)]
Total time elapsed: 0.175 seconds.
- GenomeBase
Score: 0.986667
Fitness: 1.145006
Params: {'rangemax': 100, 'rangemin': 0}
Slot [Evaluator] (Count: 1)
Name: perfect_weather - Weight: 0.50
Doc: Fitness Function for the program
Parameter: chromosome
Type: One dimensional list
Returns: Score of the function
Type: Float
Slot [Initializator] (Count: 1)
Name: G1DListInitializatorInteger - Weight: 0.50
Doc: Integer initialization function of G1DList
This initializator accepts the *rangemin* and *rangemax* genome parameters.
Slot [Mutator] (Count: 1)
Name: G1DListMutatorSwap - Weight: 0.50
Doc: The mutator of G1DList, Swap Mutator
.. note:: this mutator is :term:`Data Type Independent`
Slot [Crossover] (Count: 1)
Name: G1DListCrossoverSinglePoint - Weight: 0.50
Doc: The crossover of G1DList, Single Point
.. warning:: You can't use this crossover method for lists with just one element.
- G1DList
List size: 3
List: [96, 0, 0]
* mainGAcode.py :
This is the main code . It takes into consideration the different choices of Individual and changes fitness function respectively .
SAMPLE OUTPUT FOR MAIN CODE:
-----------------------------
Enter Your weather Love
-----------------------------------------------------------------
Do you like sunny weather ?
y
Do you like rainy weather ?
n
Do you like windy weather ?
y
Gen. 0 (0.00%): Max/Min/Avg Fitness(Raw) [0.47(0.84)/0.33(0.07)/0.39(0.39)]
Gen. 10 (10.00%): Max/Min/Avg Fitness(Raw) [1.12(0.94)/0.00(0.31)/1.10(0.93)]
Gen. 20 (20.00%): Max/Min/Avg Fitness(Raw) [1.03(0.94)/1.03(0.94)/1.03(0.94)]
Gen. 30 (30.00%): Max/Min/Avg Fitness(Raw) [1.12(0.94)/0.00(0.31)/1.10(0.93)]
Gen. 40 (40.00%): Max/Min/Avg Fitness(Raw) [1.12(0.94)/0.00(0.31)/1.10(0.93)]
Gen. 50 (50.00%): Max/Min/Avg Fitness(Raw) [1.12(0.94)/0.00(0.31)/1.10(0.93)]
Gen. 60 (60.00%): Max/Min/Avg Fitness(Raw) [1.10(0.94)/0.00(0.31)/1.06(0.92)]
Gen. 70 (70.00%): Max/Min/Avg Fitness(Raw) [1.11(0.94)/0.00(0.31)/1.08(0.92)]
Gen. 80 (80.00%): Max/Min/Avg Fitness(Raw) [1.11(0.94)/0.00(0.31)/1.08(0.92)]
Gen. 90 (90.00%): Max/Min/Avg Fitness(Raw) [1.11(0.94)/0.00(0.31)/1.08(0.92)]
Gen. 100 (100.00%): Max/Min/Avg Fitness(Raw) [1.10(0.94)/0.00(0.31)/1.06(0.92)]
Total time elapsed: 0.184 seconds.
- GenomeBase
Score: 0.940000
Fitness: 1.099513
Params: {'rangemax': 100, 'rangemin': 0}
Slot [Evaluator] (Count: 1)
Name: perfect_weather - Weight: 0.50
Doc: Fitness Function for the program
Parameter: chromosome
Type: One dimensional list
Returns: Score of the function
Type: Float
Slot [Initializator] (Count: 1)
Name: G1DListInitializatorInteger - Weight: 0.50
Doc: Integer initialization function of G1DList
This initializator accepts the *rangemin* and *rangemax* genome parameters.
Slot [Mutator] (Count: 1)
Name: G1DListMutatorSwap - Weight: 0.50
Doc: The mutator of G1DList, Swap Mutator
.. note:: this mutator is :term:`Data Type Independent`
Slot [Crossover] (Count: 1)
Name: G1DListCrossoverSinglePoint - Weight: 0.50
Doc: The crossover of G1DList, Single Point
.. warning:: You can't use this crossover method for lists with just one element.
- G1DList
List size: 3
List: [91, 0, 91]