To anybody following this project:

v0.1.0 was just released and published to Hex.

Documentation
Package

This version includes a few basic selection, crossover, and mutation methods, a text visualization module, and a Genealogy tree implementation. It has many limitations and many more features are in the works; however, this is a usable working version.

Enjoy!

v0.1.1

Documentation
Package

What’s New

• Bug Fixes
• Finalized Implementation of Selection, Mutation, and Crossover Functions
• Improved Documentation

What’s Next

• Adding Statistics to Population Struct
• Support for multiple Populations and Migration between Populations
• More comprehensive documentation

v0.1.2

Documentation
Package

What’s New

• Bug Fixes
• Improved Documentation (Including full customization guide)
• Statistics collection using built in or 3rd party modules
• Implementation of Crossover Rate, Mutation Rate, and Radiation as overridable functions to change with population parameters

What’s Next

• PERFORMANCE

From the start of this project, I understood Elixir and the BEAM are not ideal for this kind of application; however, I am really falling in love with the simplicity Elixir and this Library can offer to these kinds of problems. That being said, I know this library’s performance will likely never rival DEAP or similar implementations in languages like C or Rust; however, I know the performance itself can be improved.

If you check out some of the benchmarks in the Git Repository, you’ll see how hopelessly slow a lot of the evolutionary operators are. I’m considering 3 options for solving this problem:

1. Parallelization using Task API or Flow as well as scrutinizing my implementations of the various algorithms. This is probably the route I’m going to explore first and settle with for the next release.
2. NIFs. My research tells me this is an absolutely ideal use case for NIFs. Essentially, we just need the evolutionary operators (crossover, mutation, selection) implemented as NIFs. The operators just need to perform computation on large lists really fast and return the result. This seems like an ideal use case for NIFs. If anybody has experience writing them in Rust or C, please shoot me a message so we can discuss!
3. Ports. This option doesn’t make much sense in my opinion although if anybody can make an argument for it, I’d be willing to listen.

If you’re writing anything cool with Genex or just playing around - please let me know! I don’t expect this library to be very popular but it’s been a lot of fun to work on

Ideal use-case for Rust (and the Rustler library).

Don’t forget to look at the Matrax or whatever it was called library, it exposes some BLAS NIF functionality!

Interesting project, years ago I played with genetic algorithms and it was quite fun.
I’d also go for the combination with Rust.

If only I had time…

v0.1.4

Documentation
Package

What’s New

• Bug fixes
• 3.51x Performance Improvement in single_point crossover
• 2.35x Performance Improvement in two_point crossover
• Addition of benchmark/0 function to benchmark your algorithm
• Addition of track_history? flag to turn off and on the Genealogy tree feature (see problems for why).

What’s Next

• Exploring the idea of using the Matrex library per @OvermindDL1 suggestion. Representing the Populations and Chromosomes as matrices gives access to their REALLY fast matrix functions.
• Also began messing around with implementing the operators as NIFs last night. 2 problems I ran into: Rustler’s current release doesn’t support Erlang 22. Not a huge problem because their master branch does, more a minor inconvenience. 2nd problem is it was my first experience with Rust and I couldn’t write anything efficient to save my life .

Problems

• I had to add the track_history? flag because I couldn’t get Benchee to play nicely with the current Genealogy tree implementation. The Genealogy tree is just an Erlang digraph. The problem is Benchee runs in it’s own process and Erlang digraph’s are protected by default. I thought it would be an easy fix - considering you can make ETS table’s public - however, digraph’s protection options are only :protected and :private. If anybody has a workaround for this, I’d love to hear it!

Thanks!

v0.2.0

Documentation
Package

What’s New

• Bug Fixes
• Changed to libgraph for Genealogy Tree
• Added ability to export Genealogy to DOT file
• Removed track_history? flag
• Added a WHOLE bunch of tests so it should be pretty stable
• Improved Documentation
• Additional helper methods for evolutionary operators

What’s Next

• Configuration on run call instead of at Module definition (allows for comparison of the same problem without having to define another module).
• Still looking into performance, bear with me while I learn Rust
• Logbook which basically stores EVERY population that ever existed throughout the algorithm run time. Probably will just use ETS to start with configuration options for Redis or whatever else people want to use.

I’ve been making releases basically consistently once a day; however, I’ll be heading back to school tomorrow so the rate of development will slow down a bit. I have some plans for future features, but I won’t be able to commit as much time as I have to it in the coming weeks.

Yeah I relate there, except I work for a college instead of being a student (so done with that well over a decade ago!), we’ve been crazy getting everything ready for the new semester, like everything needed updates… ^.^

Love watching this develop! When I get time I’m planning to experiment, I love genetic algorithms and it’s been years since I’ve messed with it. ^.^

Love watching this develop! When I get time I’m planning to experiment, I love genetic algorithms and it’s been years since I’ve messed with it. ^.^

I appreciate the support! The library has been a lot of fun to mess around with. I’ve had a good time just coming up with examples and playing with the different options. Whenever you do get the chance to experiment, I’d love to hear what you come up with!!

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