Python Cuda Genetic Algorithm Package
Project description
📦pycuga
Pycuga (PYthon CUda Genetic Algorithm) provides a simple and easy package for performing island-based genetic algorithm using Python and Cuda.
Motivation
- When I worked on my previous project on Solving Maximum Satisfiability Problem using CUDA, I realised a lot of code could be reused, which save a lot of development time for solving other optimisation problems using genetic algorithm and CUDA.
Parameters
Methods currently supported | |
---|---|
Selection | Elitism, Roulette Wheel |
Crossover | Single, Double, Uniform |
Mutation | Number |
pip install pycuga
p1 = PyCUGA( isTime, time , constArr, chromosomeSize, stringPlaceholder,mutationNumber , selectionMode, crossoverMode)
p1. launchKernel(islandSize , blockSize , chromosomeNo, migrationRounds,rounds)
Examples here.
Limitations
- Use multiples of 32 (for chromosome parameters) to avoid bugs and increase efficiency.
- Island migration is limited to 1 item currently
- Lack unit testing
❗Disclaimer
This is a mini project which I've put quite a lot of time and effort into, but I can't take responsibility for any bugs nor errors.
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