About the GPEMjournal blog

This is the editor's blog for the journal Genetic Programming and Evolvable Machines. The official web site for the journal, maintained by the publisher (Springer) is here. The GPEMjournal blog is authored and maintained by Lee Spector.

Tuesday, July 2, 2024

ARC prize: a call to arms for Genetic Programming by Alberto Tonda

The Abstraction and Reasoning Corpus (ARC) is a benchmark designed to be easy to solve for humans and next to impossible for machine learning techniques which rely upon massive training data sets, like Deep Learning. Google's François Chollet, the author,  presents ARC as an attempt to push for AI algorithms able to "learn like humans" [1], or in other words, able to solve tasks after seeing just a small number of training instances, exploiting innate capacities to reason on geometry and number [2]. Just a few weeks ago, Chollet announced a Kaggle challenge on ARC, with a prize of 1 million $ [3] and a first deadline in November 2024, although submissions are already open [4].

I am not affiliated with the prize in any way, but I think this could represent a valuable opportunity for the GP/EA community: the tasks in ARC can be solved through *program synthesis*, as stated by Chollet himself [5], a function GP excels at; and the capacity of learning from a few samples is another strong suite of GP. Not to mention, just participating in the contest and comparing against other approaches could lead to cross-fertilization of new ideas, and even help promote GP as a robust AI alternative to the now more prominent DL approaches.

After discussing with colleagues, I decided to spread the news far and wide, in the hope that more and more people from our community would decide to take up the challenge. The current state of the art performance on ARC is still low (less than 40% accuracy on test at the time of writing), so the entry barriers should not be that high: now it's a good moment for GP researchers to take on the world and test our mettle!

[1] https://arxiv.org/pdf/1911.01547 
[2] https://aiguide.substack.com/p/why-the-abstraction-and-reasoning
[3] https://arcprize.org/competition
[4] https://www.kaggle.com/competitions/arc-prize-2024/
[5] https://arcprize.org/guide

Sunday, June 2, 2024

Applications of Genetic Programming

An oft posed question is how much is genetic programming used, "for real"? https://gpbib.cs.ucl.ac.uk/gp-html/jaws30_reply.html Today, although many papers propose new types of GP, most are about applying GP.  Many papers use real world datasets to show how good a novel form of GP is or to compare GP and other AI approaches.   Instead lets concentrate upon papers where GP is just being used and the application itself is the important thing.

Of course most industrialists are not interested in papers.  Indeed they may have sound commercial reasons for not publicising their results or even what they are interested in. Which always means numbers based on published work will be an underestimate.

Nonetheless, taking data for 2023 in the genetic programming bibliography https://gpbib.cs.ucl.ac.uk/ today as typical, about 38% (pm 5%) of papers are on applications.  About a quarter of all GP papers are on: Medicine, Civil Engineering or Material Science, often with an environmental or sustainability emphasis.

Sunday, May 12, 2024

Humies 2024, closes Friday 31 May

A quick reminder GP+EM articles published after 2 June 2023 are eligible to enter this year's Human Competitive awards (competition closes to new entries on Friday May 31). Full details on line: https://human-competitive.org/call-for-entries

As with last year, the Humies will be held in hybrid mode, i.e. both in person in Melbourne and online, via video link. Prize money will be sent to the winners via wire transfer.

Entries listed in https://human-competitive.org/awards

The finalists who will battle it out to convince the judges they should be the winner have been announced on  https://human-competitive.org/awards


Tuesday, August 22, 2023

Continuous publication

Beginning with Volume 24, Genetic Programming and Evolvable Machines has adopted a "continuous publication" model, in which articles are published in open issues as soon as their production processes are complete. 

In this context it no longer seems to make sense to post new issue announcements on this blog. 

To see the latest published articles, please check the Volumes and Issues section of the journal's website.

Friday, February 17, 2023

Tuesday, November 15, 2022

GPEM 23(4) is now available

The fourth issue of Volume 23 of Genetic Programming and Evolvable Machines is now available for download.

This issue includes papers in the Special Issue on Evolutionary Computation in Art, Music and Design.

It contains:

A novel tree-based representation for evolving analog circuits and its application to memristor-based pulse generation circuit
by Xinming Shi, Leandro L. Minku, and Xin Yao

Using estimation of distribution algorithm for procedural content generation in video games
by Arash Moradi Karkaj and Shahriar Lotfi

Complexity and aesthetics in generative and evolutionary art
by Jon McCormack and Camilo Cruz Gambardella

Experiments in evolutionary image enhancement with ELAINE
by João Correia, Daniel Lopes, Leonardo Vieira, Nereida Rodriguez-Fernandez, Adrian Carballal, Juan Romero and Penousal Machado

Melanie Mitchell: Artificial intelligence—a guide for thinking humans
by Didem Özkiziltan

Machado, Romero and Greenfield (editors): Artificial intelligence and the arts
by Anna Jordanous

The evolution of complexity
by Emily Dolson

Ying Bi, Bing Xue, Mengjie Zhang: Genetic programming for image classification—an automated approach to feature learning
by Amelia Zafra

GPEM 23(3) is now available

The third issue of Volume 23 of Genetic Programming and Evolvable Machines is now available for download

This is a Special Issue on Highlights of Genetic Programming 2021 Events, edited by Leonardo Trujillo, Nuno Lourenço, Ting Hu, and Mengjie Zhang.

It contains:

Editorial Introduction
by Leonardo Trujillo, Ting Hu, Nuno Lourenço, and Mengjie Zhang

Interpretability in symbolic regression: a benchmark of explanatory methods using the Feynman data set
by Guilherme Seidyo Imai Aldeia and Fabrício Olivetti de França

Evolutionary approximation and neural architecture search
by Michal Pinos, Vojtech Mrazek, and Lukas Sekanina

Applying genetic programming to PSB2: the next generation program synthesis benchmark suite
by Thomas Helmuth and Peter Kelly

Severe damage recovery in evolving soft robots through differentiable programming
by Kazuya Horibe, Kathryn Walker, Rasmus Berg Palm, Shyam Sudhakaran, and Sebastian Risi

A grammar-based GP approach applied to the design of deep neural networks
by Ricardo H. R. Lima, Dimmy Magalhães, Aurora Pozo, Alexander Mendiburu, and Roberto Santana