https://github.com/cicirello/optimize-ga-operators
Experiments for paper: Optimizing Genetic Algorithms Using the Binomial Distribution
https://github.com/cicirello/optimize-ga-operators
binomial-distribution binomial-random-variable binomial-random-variates bitflip bitflip-mutation crossover evolutionary-algorithms genetic-algorithm-control-loop genetic-algorithms mutation uniform-crossover
Last synced: 3 months ago
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Experiments for paper: Optimizing Genetic Algorithms Using the Binomial Distribution
- Host: GitHub
- URL: https://github.com/cicirello/optimize-ga-operators
- Owner: cicirello
- License: gpl-3.0
- Created: 2023-05-10T16:27:48.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-12-04T21:19:28.000Z (about 1 year ago)
- Last Synced: 2025-03-19T06:04:03.721Z (10 months ago)
- Topics: binomial-distribution, binomial-random-variable, binomial-random-variates, bitflip, bitflip-mutation, crossover, evolutionary-algorithms, genetic-algorithm-control-loop, genetic-algorithms, mutation, uniform-crossover
- Language: Java
- Homepage: https://www.cicirello.org/publications/cicirello2024ecta2.html
- Size: 1.56 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Code to reproduce the experiments from: Optimizing Genetic Algorithms Using the Binomial Distribution
Copyright © 2023-2024 Vincent A. Cicirello
This repository contains code to reproduce the experiments, and analysis of
experimental data, from the following paper:
> Vincent A. Cicirello. 2024. [Optimizing Genetic Algorithms Using the Binomial Distribution](https://www.cicirello.org/publications/cicirello2024ecta-optimize-ga.pdf). *Proceedings of the 16th International Joint Conference on Computational Intelligence*, pages 159-169. November 2024. doi:[10.5220/0013038300003837](https://doi.org/10.5220/0013038300003837).
| __Publication__ | [](https://doi.org/10.5220/0013038300003837) |
| :--- | :--- |
| __License__ | [](LICENSE) |
## Dependencies
The experiments depend upon the following libraries, which are automatically downloaded from
Maven Central during the build process:
* [Chips-n-Salsa](https://chips-n-salsa.cicirello.org/) 7.0.0
* [JavaPermutationTools](https://jpt.cicirello.org/) 6.0.0
* [ρμ](https://rho-mu.cicirello.org) 4.1.0
* [org.cicirello.core](https://core.cicirello.org) 2.7.0
## Requirements to Build and Run the Experiments
To build and run the experiments on your own machine, you will need the following:
* __JDK 17__: I used OpenJDK 17, but other distributions should be fine.
* __Apache Maven__: In the root of the repository, there is a `pom.xml`
for building the Java programs for the experiments. Using this `pom.xml`,
Maven will take care of downloading the exact version of
[Chips-n-Salsa](https://chips-n-salsa.cicirello.org/) (release 7.0.0)
and its dependencies that were used in the experiments.
* __Python 3__: The repository contains Python programs that were used to
process the raw data for the paper. If you want to run the Python programs,
you will need Python 3.
* __Make__: The repository contains a Makefile to simplify running the build,
running the experiment's Java programs, and running the Python program to
analyze the data. If you are familiar with using the Maven build tool,
and running Python programs, then you can just run these directly, although
the Makefile may be useful to see the specific commands needed.
## Building the Java Programs
The source code of the Java programs implementing the experiments
is in the [src/main/java](src/main/java) directory. You can build
the experiment programs in one of the following ways.
__Using Maven__: Execute the following from the root of the
repository.
```shell
mvn clean package
```
__Using Make__: Or, you can execute the following from the root
of the repository.
```shell
make build
```
## Running the Experiments
If you just want to inspect the data from my runs, then you can find that output
in the [/data](data) directory. If you instead want to run the experiments yourself,
you must first follow the build instructions. Once the jar of the experiments is
built, you can then run the experiments with the following executed at the root of
the repository:
```shell
make experiments
```
If you don't want to overwrite my original data files, then first change the variable
`pathToDataFiles` in the `Makefile` before running the above command.
## Analyzing the Experimental Data
To run the Python programs that process the raw data and generate the figures
from the paper, you need Python 3 installed. The source
code of the Python programs is found in the [src/analysis](src/analysis)
directory. To run the analysis, execute the following at the root of the
repository:
```shell
make figures
```
This make command will also take care of installing any required Python packages
if you don't already have them installed.
If you want to generate the figures in `pdf` format, then after executing the
above, proceed to execute the following (which assumes that you have `epstopdf`
installed):
```shell
make epstopdf
```
If you don't want to overwrite my original data files, and figures, then change the
variable `pathToDataFiles` in the `Makefile` before running the above commands.
## Other Files in the Repository
There are other files, potentially of interest, in the repository, including:
* `system-stats.txt`: This file contains details of the system I
used to run the experiments, such as operating system, processor
specs, Java JDK and VM. It is in the [/data](data) directory.
## License
The code to replicate the experiments from the paper, as well as the
Chips-n-Salsa library and its dependencies, are licensed under
the [GNU General Public License 3.0](https://www.gnu.org/licenses/gpl-3.0.en.html).