This code is created to support exercises and tutorials in a taught module on artificial intelligence. The implementation is meant to be conceptually simple and flexible, at the expense of speed. Also, error checks have generally not been implemented, and there has been no attempt to pack it up as a proper python module for reuse.
The code is crude. Please suggest improvements.
- BinaryGA
- all the necessary demoes to optimise floating point functions using a binary GA
- ContinuousGA
- builds on BinaryGA adding demoes for floating point chromosomes
- TournamentGA
- this variant demonstrates tournament selection. The API of the mating function is not compatible, and the population is not sorted.
- Simulation
- demo of GA to simulate economic markets
- v0.2.0 - prepared for session 17 March 2022
- v0.1.0 - prepared for session 3 March 2022
- the branch v0.1 contains bugfixes