pycuga 0.0.14

📦pycuga

Pycuga (PYthon CUda Genetic Algorithm) provides a package for performing island-based genetic algorithm on Python and CUDA.

• Methods for migartion, selection and mutation are implemented already. Users only need to pick the method during initialisation.
• User needs to define evaluation using Cuda code.

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

pip install pycuga

p1 = PyCUGA(constArr, chromosomeSize, stringPlaceholder, mutationNumber, selectionMode, crossoverMode)
p1. launchKernel(islandSize, blockSize , chromosomeNo, isTime, timeAllowed, migrationRounds, rounds)

• constArr (numpy array): defines the details of the problem

• chromosomeSize (int): number of variables in a chromosome

• stringPlaceholder (string): defines the evalutaion method and other constants

• mutationNumber (int): /

• selectionMode (string): "elitism" or "roulettewheel"

• crossoverMode (string): "one" or "two" or "uniform"

• islandSize (int): size of an island

• blockSize (int): Cuda block size

• chromosomeNo (int): number of chromosomes

• migrationRounds (int): number of rounds per migration

• isTime (bool): stopped by time or number of rounds

• timeAllowed (int): seconds allowed

• rounds (int): total number of rounds

Examples here.

Colab MaxSat example 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.