Skip to content

Latest commit

 

History

History
60 lines (43 loc) · 2.46 KB

README.md

File metadata and controls

60 lines (43 loc) · 2.46 KB
Raw

MSc Thesis Code - Implementation and Evaluation of Recent Neuroevolution Algorithms

This repository contains the code for my MSc thesis on neuroevolution. It is a framework for visualizing and testing different neuroevolution algorithms on benchmark problems.

Setup

The framework is written in Rust. To run it, you need to have Rust and Cargo installed on your system. , which you can do by following the instructions on the official website.

After installing Rust, and cloning the repository, you can build the project by running the following command in the project's root directory:

cargo build --release

This will take care of downloading all the dependencies and building the project. The resultin framework binary will be located in the target/release directory as main.

Usage

Neuroevolution framework for testing algorithms on benchmark problems.

Usage: main [OPTIONS] <ALGORITHM> <PROBLEM>

Arguments:
  <ALGORITHM>  The algorithm to test [possible values: oneplusonena, bna, neat, cmaes]
  <PROBLEM>    the benchmark problem [possible values: half, quarter, two-quarters, square, cube, xor, pole-balancing, proben1, local-opt]

Options:
  -r, --resolution <RESOLUTION>  Resolution, when applicable [default: 1000]
  -i, --iterations <ITERATIONS>  Number of iterations [default: 500]
  -n, --neurons <NEURONS>        Number of neurons, when applicable [default: 1]
  -g, --gui                      Display visualization
  -f, --file <FILE>              Configuration file
  -o, --output <OUTPUT>          Results output file
  -t, --test-runs <TEST_RUNS>    Number of runs
  -e, --error-tol <ERROR_TOL>    Max fitness tolerance [default: 0.02]
  -s, --stagnation <STAGNATION>  Max stagnation
  -h, --help                     Print help
  -V, --version                  Print version

Examples

To run the BNA algorithm on the Half problem, with 200 iterations and 1 neuron, and to visualize the evolution process, you can run the following command:

./target/release/main bna half -i 200 -n 1 -g

To run the NEAT algorithm on the XOR problem, with 300 iterations, using the configuration file configs/neat/xor.toml and saving the results of 100 test runs to the file results.csv, you can run the following command:

./target/release/main neat xor -i 300 -f configs/neat/xor.toml -o results.csv -t 100