About GP.Lab

Hi there and welcome to the GP.Lab blog.

So what's "GP.Lab"?

It's a Genetic Programming Workbench and a work in progress. Besides being a fun, non-profit software research project its main goal is to investigate machine learning (Genetic Programming in particular) and its applications to automatically solve a range of problems.

GP.Lab implements a modular architecture: Individual tasks or problems are essentially plug-ins to the core app containing everything related to the specific problem to be solved such as
  • Specific operators  (Robot steering commands, access to data sources)
  • A visualization appropriate for the task
  • Additional logic required to evaluate programs (e.g. a simulation environment)
The core app implements basic functionality such as the core GP kernel algorithm, methods for managing the GP population, as well as a bunch of statistical tools to provide insight into the simulated evolutionary process.

Initial Set of Tasks

At this point GP.Lab includes implementations for the following problems:
  1. Symbolic regression (Math. functions)
  2. Santa Fe Trail problem
  3. RoboRail
  4. Collector
Tasks #1 and #2 are commonly used in machine learning and already described in detail in numerous places. Both are neither terribly interesting nor complex - which is great when using them as a test bed for developing and extending the core functionality of GP.Lab!

The RoboRail and Collector tasks are essentially about controlling robots in simulated environments. The goal is to automatically find robust solutions for path finding and item collecting tasks.

Additional modules are currently in various stages of planning/implementation/testing.


Some Links

If you would like to learn more about Genetic Algorithms - and Genetic Programming in particular - you might want to read this wikipedia article or check out these books:

The project has a dedicated YouTube channel at youtube.com/user/virtualspecies demonstrating some of the GP.Lab modules currently available.

Where do we go from here?

Future posts will cover in more detail the Genetic Programming implementation used by GP.Lab, individual tasks, and more. The app itself will also be made available for download.

At this point there are no plans to open up the source code of GP.Lab, though.

Share and Enjoy!

Comments

Post a Comment

Popular posts from this blog

GP.Lab: Dark Silicon Update

Image
It's been a while since my last update. However, it appears GP.Lab is coming out of hibernation these days.. For now there's a new preview to share featuring a moderately modernized app with support for macOS' new(ish) Dark Mode as well as native support for Apple Silicon Macs . Naturally Intel Macs are still supported, as are macOS versions equal to or newer than macOS 11 ("Big Sur"). The latest preview also features a new Sorting module for (well..) sorting an array of 10 integer numbers in ascending order. There's more work to be done on this one so please treat it kindly as an early preview. The Collector module has been improved and bugs impeding the sensory input of our feeble robot were removed - resulting in quite some improvement in its ability to actually collect items and deliver them to the home base. See it in action here: https://youtu.be/0WSjwM38i2Q And one of these days I've got to actually describe all of the modules (tasks) included
Read more

GP.Lab: Public Preview

Image
As I mentioned earlier GP.Lab is freeware - assuming that term still makes sense in today's diverse landscape of monetizing software products.. Anyhow: A preview release of GP.Lab is now available for download here . A Mac running macOS 10.11 or newer is required. The current implementation has modest memory requirements but you'll want to run it on a machine with plenty cores to speed up the evaluation of programs in the population. Quick Guide When the app is launched the Project Selector is presented. Select the task you would like the machine learning system to tackle. A brief description is provided for each task and additional details can be found in earlier blog posts. Customize Run Parameters such as program and population size, selection mode and genetic operator probabilities in the next window. The tournament size and elitism parameters should be handled with extra care: both result in quick loss of diversity in the simulated gene pool when chosen too
Read more