dannyi96 / pygenetic Goto Github PK
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License: MIT License
An efficient generic, user-friendly Python Genetic Algorithm API
License: MIT License
Convert user inputs from UI to equivalent python code
Handling of multiple crossovers and mutation handlers with different probabilities
We may need to go for running it in a cluster. Also try to optimise the various RDD actions to a minimum
Hello, having a little trouble using your library so I will probably fork it.
Problem
Pygenetic is not using a version of matplotlib which can be installedin python 3.8
Version 2.2.2 of matplotlib is not available
To reproduce:
Make or access a python 3.8 install.
Attempt to install pygenetic, or even matplotlib 2.2.2
Get told it is not possible, as matplotlib 2.2.2 cannot be found.
I will open a pull request once I know updating to a newer version will not break anything.
One of the outputs of the crossovers for inputs ([1, 0, 7, 2, 6, 4, 3, 5], [4, 5, 3, 6, 0, 1, 2, 7]) remained ([1, 0, 7, 2, 6, 4, 3, 5], [4, 5, 3, 6, 0, 1, 2, 7]).
Termination of iterations when fitness type is EQUAL for flask app
Need to improve the appearance of the textarea boxes
Implementation of a Simple Basic High Level GA Api
Create some ipython notebook files of Step by Step Code Examples with explanation ( nQueens and TSP )
Roulette Wheel Selection
Stochastic Universal Sampling (SUS)
Tournament Selection
Rank Selection
Random Selection
Need to integrate TravisCI in our project
Currenty(with distinct crossover and swap mutation), n Queens problem doesn't get solved for even small numbers like n=16
We need to include other methods or figure other better GA parameter configuration to solve problem for larger values of n
Aim: Incorporate different map reduce architectures for GA Evolution
Indicated by Green/Red Colour
Need to implement ga
which is currently a global variable as a cookie to support multiple users accessing it at the same time
Need to create a good README section
Need to add scrolling to the bottom of page effect as and when each generation data is received.
Implementing polling to get each generation data on the UI immediately as soon as it is computed.
Need to include various statistics of GA to visualize best/avg/max/min fitness over different generations, etc
So users can set different optimisations
Handle different fitness types - MAXIMUM, MINIMUM ( with and without threshold), EQUAL
Making the user interface of main page of our web application (to get details of the GA to be executed)
We have to modify the formula a bit or use a different adaptive method to see a noticeable difference
mutation_rates [0.5859037956712174, 0.5890376972849537, 0.5903909891318503, 0.5916622332897328, 0.5910757976952127, 0.5911468264032488, 0.5913217861004001, 0.5912461103370071, 0.5916085995081001, 0.5918987263324281, 0.5922810038663388, 0.5924175437299303, 0.5919739575621374, 0.5920402257273272, 0.5917604537840548, 0.5924933255524385, 0.5923069473804097, 0.5923912569728325, 0.5926584181942748, 0.5922699332103435]
Need to explore more application/examples of GA and implement it using our API
Need to have a final review of the entire code and work on publishing it on pypi
Explore and implement some ML technique using GA
Download equivalent code as a python file on pressing the form submit button
Store best fitness throughout all generations ( applicable to all selection types )
Chromosome Factory testing
Utils testing
Population Testing
Statistics Testing
Evolution Testing
GAEngine Testing
Need to Redesign selection handlers to work based on fitness_type max, min and equal
Implement Jquery functionality to minus(-) button
May fail due to the use of the list p
which contains relative fitnesses
Need to have a population control parameter in GAEngine
If set to true - always ensure that population in each generation is same ( in case it isn't , it selects only needed elements from top of the list )
If set to false - Population can take any number of members. (i.e. - can always be constantly increasing ). User can optionally set a UPPER_LIMIT here. Useful for pySpark Evolutions
Compare different GAs executed based on
And completion of final report
Implementation of GA Optimisations like adaptive mutation
Need to change evolution using pyspark class since it supports only limited selection - best, worst and equal to selection criteria. Need to ignore other given selection criteria.
Need to change the interfaces as well
Need to allow option for custom crossover, mutation, selection and evolution from UI
2D Chromosome Factory which are popularly used in many problems like sudoku solving
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