Tag: ga

Genetic Algorithms, Pyevolve, Python

The Interactive Robotic Painting Machine !

I’m glad to announce a project created by Benjamin Grosser called “Interactive Robotic Painting Machine“. The machine uses Python and Pyevolve as it’s Genetic Algorithm core, the concept is very interesting:

What I’ve built to consider these questions is an interactive robotic painting machine that uses artificial intelligence to paint its own body of work and to make its own decisions. While doing so, it listens to its environment and considers what it hears as input into the painting process. In the absence of someone or something else making sound in its presence, the machine, like many artists, listens to itself. But when it does hear others, it changes what it does just as we subtly (or not so subtly) are influenced by what others tell us.

Read more about the project in the Benjamin Grosser website.

 

Genetic Algorithms, genetic programming, News, Science

New issue of SIGEVOlution (Volume 4 Issue 3)

The new issue of SIGEVOlution (the newsletter of ACM Special Interest Group on Genetic and Evolutionary Computation) was released:

In this release you can read about:

Issues in applying computational intelligence
By Arthur Kordon

JavaXCSF
By Patrick O. Stalph, Martin V. Butz

And a lot of information about new PhD theses, new journal issues and about events to come.

Genetic Algorithms, News, Science

Meanwhile, at the Hall of Justice!

UPDATE 05/10: there is an article in the Physorg too.

Sometimes we face new applications for EC, but for this I was not expecting, from Eurekalert:

WASHINGTON, Oct. 5 — Criminals are having a harder time hiding their faces, thanks to new software that helps witnesses recreate and recognize suspects using principles borrowed from the fields of optics and genetics.

(…)

His software generates its own faces that progressively evolve to match the witness’ memories. The witness starts with a general description such as “I remember a young white male with dark hair.” Nine different computer-generated faces that roughly fit the description are generated, and the witness identifies the best and worst matches. The software uses the best fit as a template to automatically generate nine new faces with slightly tweaked features, based on what it learned from the rejected faces.

“Over a number of generations, the computer can learn what face you’re looking for,” says Solomon.

Read the full article here.

I'm starting a new course "Machine Learning: Foundations and Engineering" for 2024.