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, 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

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

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

BOOK REVIEW
The evolution of complexity
by Emily Dolson

BOOK REVIEW
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

Tuesday, July 12, 2022

GECCO 2022 impact award

The ten year GECCO 2022 award, announced yesterday, for most impact paper formed the basis for a paper in GP+EM:

"Better GP benchmarks: community survey results and proposals" by David R. White and James McDermott and Mauro Castelli and Luca Manzoni and Brian W. Goldman and Gabriel Kronberger and Wojciech Jaskowski and Una-May O'Reilly and Sean Luke. Genetic Programming and Evolvable Machines, 14(1):3-29, 2013.
http://gpbib.cs.ucl.ac.uk/gp-html/White_2013_GPEM.html

Sunday, May 15, 2022

GPEM 23(2) is now available

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

It contains:

Blood glucose prediction using multi-objective grammatical evolution: analysis of the “agnostic” and “what-if” scenarios
by Sergio Contador, J. Manuel Colmenar, Oscar Garnica, J. Manuel Velasco, J. Ignacio Hidalgo

On the performance of the Bayesian optimization algorithm with combined scenarios of search algorithms and scoring metrics
by Ciniro A. L. Nametala, Wandry R. Faria, Benvindo R. Pereira Júnior

Evolving cellular automata schemes for protein folding modeling using the Rosetta atomic representation
by Daniel Varela, José Santos

Genetic programming for iterative numerical methods
by Dominik Sobania, Jonas Schmitt, Harald Köstler, Franz Rothlauf

GP-DMD: a genetic programming variant with dynamic management of diversity
by Ricardo Nieto-Fuentes, Carlos Segura

Saturday, April 2, 2022

GPEM 23(1) is now available

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

It contains:

Editorial introduction
by Lee Spector

Acknowledgment to reviewers (2021)
by Lee Spector

Inference of time series components by online co-evolution
by Danil Koryakin, Sebastian Otte, Martin V. Butz

Constant optimization and feature standardization in multiobjective genetic programming
by Peter Rockett

Genetic programming convergence
by W. B. Langdon

Automatic generation of regular expressions for the Regex Golf challenge using a local search algorithm
by André Almeida Farzat, Márcio Oliveira Barros

Generating networks of genetic processors
by Marcelino Campos, José M. Sempere

BOOK REVIEW
Robert Elliott Smith: Rage Inside the Machine—the prejudice of algorithms, and how to stop the internet making bigots of us all
by Walid Magdy

BOOK REVIEW
Artificial intelligence for fashion, Leanne Luce, Apress 2019, ISBN 978-1-4842-3930-8 how AI is revolutionizing the fashion industry
by Grace Buttler

Friday, December 31, 2021

Call for Papers: Thirtieth Anniversary of Genetic Programming: On the Programming of Computers by Means of Natural Selection

In 1992, John R. Koza published his first book on Genetic Programming (GP): "Genetic Programming: On the Programming of Computers by Means of Natural Selection" [1]. This ground-breaking book paved the way for the establishment of a new field of study. It influenced the work of thousands of researchers and practitioners worldwide, many of whom aimed to continue the exploration, formalization and improvement of the original formulation of GP and/or to apply GP to challenging problems.  

We aim to celebrate the 30th anniversary of [1] with a special issue that focuses on the multiple impacts that the book had, and is still having, on the GP field. We hope that the special issue will illustrate many of the ways in which the ideas proposed by Koza in [1] have influenced and are still influencing the GP community. 

[1] Koza, J. R. (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA, USA: MIT Press. ISBN: 0-262-11170-5

TOPICS OF INTEREST

We are open to a broad range of submissions that, in various ways, help us better understand the impact of [1] on the GP community. Submissions that would be welcomed might for example present: 

  • High quality review articles of [1] and of subsequent books or other types of publications, that may include a deep discussion of the relationship, similarities and differences with [1].
  • Discussions of ways in which current techniques and practices are similar to or different from those recommended in [1], and the ways in which the methods presented in [1] have evolved towards more modern and effective methods.
  • Challenges that [1] regarded as open, and the extent to which they are now completely fulfilled or that still constitute open issues for the GP field. 
  • Ground-breaking ideas that were proposed in [1] and that have inspired research in the past, or may inspire new research in the future.
  • Ideas in [1] that have received little attention to date, which might be beneficial to revisit.
  • Works that deal with the impact that [1] has had in applied domains, exploring and/or surveying the achievements and limits of the methods and ideas proposed in [1] for solving real-world problems.
  • Applications that directly build on the techniques described in [1] and are able to achieve human-competitive results.

TENTATIVE TIMELINE

  • Submission deadline: 30 July 2022
  • Initial reviews: 15 October 2022
  • Resubmissions: 10 December 2023
  • Final notifications: 10 February 2023

GUEST EDITORS

Leonardo Vanneschi 
NOVA IMS, Universidade Nova de Lisboa, Portugal
lvanneschi@novaims.unl.pt

Leonardo Trujillo 
Instituto Tecnológico de Tijuana, Mexico
leonardo.trujillo@tectijuana.edu.mx

SUBMISSION GUIDELINES

Authors are encouraged to submit high-quality, original work that has neither appeared in, nor is under consideration by other journals. All papers will be reviewed following standard reviewing procedures for the Journal. Papers must be prepared in accordance with the Journal guidelines: www.springer.com/10710.

Submit manuscripts to: http://GENP.edmgr.com.  Select “S.I. Thirtieth Anniversary of Genetic Programming: On the Programming of Computers by Means of Natural Selection” as the article type or when asked if the article is for a special issue.

Springer provides a host of information about publishing in a Springer Journal on our Journal Author Resources page, including  FAQs,  Tutorials along with Help and Support.

Additional information can be found on the official Springer Call for Papers.

Sunday, November 7, 2021

GPEM 22(4) is now available

The fourth issue of Volume 22 of Genetic Programming and Evolvable Machines is now available for download. This is a Special Issue on Highlights of Genetic Programming 2020 Events, edited by Miguel Nicolau, Ting Hu, Mengjie Zhang, and Nuno Lourenço.

It contains:

Highlights of genetic programming 2020 events
by Miguel Nicolau

Evolving continuous optimisers from scratch
by Michael A. Lones

Evolutionary algorithms for designing reversible cellular automata
by Luca Mariot, Stjepan Picek, Domagoj Jakobovic & Alberto Leporati 

A semantic genetic programming framework based on dynamic targets
by Stefano Ruberto, Valerio Terragni & Jason H. Moore 

Relationships between parent selection methods, looping constructs, and success rate in genetic programming
by Anil Kumar Saini and Lee Spector

EvoStencils: a grammar-based genetic programming approach for constructing efficient geometric multigrid methods
by Jonas Schmitt, Sebastian Kuckuk & Harald Köstler 

Semantically-oriented mutation operator in cartesian genetic programming for evolutionary circuit design
by David Hodan, Vojtech Mrazek & Zdenek Vasicek 

Evolving hierarchical memory-prediction machines in multi-task reinforcement learning
by Stephen Kelly, Tatiana Voegerl, Wolfgang Banzhaf & Cedric Gondro 

Graph representations in genetic programming
by Léo Françoso Dal Piccol Sotto, Paul Kaufmann, Timothy Atkinson, Roman Kalkreuth & Márcio Porto Basgalupp