Author: Drew McCormack
Last Updated: 1 December, 2017
An exploratory Swift framework for Genetic Programming.
A branch of machine learning in which simple programs or mathematical functions compete to solve a problem, evolving along the lines of Darwinian evolution. A population of solutions can cross pollinate and mutate, and so evolve through generations to 'fitter' descendants.
For a detailed overview, see A Field Guide to Genetic Programming.
- Simple scalar mathematical expressions, comprised of basic operators like addition and multiplication, decimal constants, and variables
- Mathematical expressions are trees of value types (structs)
- The basic elements of the Genetic Programming (GP), namely initial population generation, evolutionary operators including crossover and mutation
- Storing of populations and expressions using the Swift
Codableprotocol - Basic unit tests
- A basic regression test showing how a function can be fitted by genetic programming
- There are few mathematical operators at this point, though they are very easy to add. Useful would be division and trigonometic functions
- The plan is to support vector expressions, and perhaps even matrices
Anomalii can be installed with the Swift Package Manager, or by building the framework target in Xcode.