GPEM journal

Section: Comments and Correspondence The Comments and Correspondence Collection of Genetic Programming and Evolvable Machines offers a space for short-form articles that engage with recently ...

Two new Sections are open for submissions in GPEM:

* Comments and Correspondence: link.springer.com/collections/...

* Perspectives and Vision: link.springer.com/collections/...

Introducing look-ahead into relocation rules generated with genetic programming for the container relocation problem - Genetic Programming and Evolvable Machines The container relocation problem is a critical combinatorial optimisation problem in warehouses and container ports. The goal is to retrieve all containers while minimising unnecessary relocations. As this problem is NP-hard, various heuristics have been proposed, including relocation rules (RRs), simple constructive heuristics that iteratively build solutions by determining how containers should be relocated within the yard for efficient retrieval. However, manually designing effective RRs is challenging, leading to the use of genetic programming to generate them automatically. A key limitation of both manually and automatically designed RRs is their restricted problem view and limited decision-making scope. This often results in suboptimal relocations, negatively impacting future operations and overall efficiency. A crucial aspect of RR design is defining effective relocation schemes that enhance decision-making by considering the long-term impact of relocations. This study investigates several relocation schemes that provide RRs with lookahead capabilities, enabling them to anticipate future consequences and make more informed moves. In addition to two standard schemes, four novel relocation schemes are introduced and evaluated using an established problem set. The results demonstrate that properly adapting relocation schemes can significantly enhance the performance of automatically designed RRs, leading to significantly better results.

And including:

Introducing look-ahead into relocation rules generated with genetic programming for the container relocation problem

Marko Ðurasević, Mateja Ðumić, Francisco Javier Gil Gala and Domagoj Jakobović

link.springer.com/article/10.1...

On fitting numerical features into probabilistic distributions to represent data for fuzzy pattern trees - Genetic Programming and Evolvable Machines Fuzzy Pattern Trees (FPTs) are symbolic tree-based structures whose internal nodes are fuzzy operators, and the leaves are fuzzy features, which enhance interpretability by representing data with meaningful fuzzy terms. However, conventional FPT approaches typically employ uniformly distributed membership functions, which often fail to accurately represent features in real-world datasets. In this work, we propose an automatic method to adapt the bounds of fuzzy features based on their data distributions, with a focus on a simple triangular membership scheme. We evaluate our approach across 11 benchmark classification problems, incorporating six parsimony pressure methods to promote more compact solutions. Our results demonstrate that the adapted fuzzification scheme, beyond improving interpretability, consistently yields models that better balance accuracy and size when compared to uniform representations, appearing on the Pareto front 20 times, while the second-best scheme appeared only 15 times.

Including:

On fitting numerical features into probabilistic distributions to represent data for fuzzy pattern trees

Allan de Lima, Juan FH Albarracín, Douglas Moto Dias, Jorge Amaral, and Conor Ryan

link.springer.com/article/10.1...

Quality-diversity in problems with composite solutions: a case study on body–brain robot optimization - Genetic Programming and Evolvable Machines When considering those optimization problems where the solution is a combination of two parts, as, e.g., the concurrent optimization of the body and the brain of a robotic agent, one might want to solve them “in a quality-diversity (QD) way”, i.e., obtaining not just one very good solution, but a set of good and diverse solutions. We call them QD composite problems, and we propose a general formulation for them, as well as a set of indexes useful for comprehensively assessing solutions by measuring both quality and diversity. We experimentally compare a few QD evolutionary algorithms (EAs) on a case study of body–brain optimization of simulated robots, including several variants of MAP-elites (ME), a popular and effective EA for QD. We also propose a novel ME variant, called coevolutionary MAP-elites (CoME), that internally employs two populations, one for each part of the solution, and enforces diversity on them through user-provided descriptors, as the underlying ME does. CoME, instead of blindly combining all the respective parts to obtain full solutions, adopts a specific mapping strategy that is based on the location of each solution part in the respective descriptors space. The results of our comparative analysis show that ME works well in QD composite problems, but only if two archives, instead of just one, are employed, one for each part of the solution. Moreover, we show that the use of multi-archive variants of ME, e.g., CoME, can provide insights on the interplay between the two parts of the solution for the problem at hand, shedding light on key dynamics in co-evolution.

Including:

Quality-diversity in problems with composite solutions: a case study on body–brain robot optimization

Eric Medvet, Samuele Lippolis, and Giorgia Nadizar

link.springer.com/article/10.1...

Editorial Introduction to the Special Issue on Evolutionary Computation in Art, Music and Design - Genetic Programming and Evolvable Machines Genetic Programming and Evolvable Machines -

New special issue of GPEM on Evolutionary Computation in Art, Music and Design!

Edited by Penousal Machado and Juan Romero

link.springer.com/article/10.1...

Kenichi Morita: Reversible world of cellular automata - Genetic Programming and Evolvable Machines Genetic Programming and Evolvable Machines -

New book review, freely available in GPEM:

“Reversible world of cellular automata” by Kenichi Morita, reviewed by Tomas Rokicki

link.springer.com/article/10.1...

Acknowledgment to reviewers (2024) - Genetic Programming and Evolvable Machines Genetic Programming and Evolvable Machines -

GPEM Journal sends acknowledgements and thanks to recent reviewers (too many to list here!):

link.springer.com/article/10.1...

GPEM Journal has a new CFP for a special issue in Generative AI and Evolutionary Computation for Software Engineering!

This will be edited by Dominik Sobania

See Leo's blogpost:
gpemjournal.blogspot.com/2025/06/call...

And special issue page:
link.springer.com/collections/...

An investigation into structured grammatical evolution initialisation - Genetic Programming and Evolvable Machines A key ingredient in any successful genetic programming is robust initialisation. Many successful initialisation methods used in genetic programming have been adapted to use with grammatical evolution,...

* Aidan Murphy, Mahsa Mahdinejad, Anthony Ventresque & Nuno Lourenço, An investigation into structured grammatical evolution initialisation: link.springer.com/article/10.1...

A comparison of representations in grammar-guided genetic programming in the context of glucose prediction in people with diabetes - Genetic Programming and Evolvable Machines The representation of individuals in Genetic Programming (GP) has a large impact on the evolutionary process. In previous work, we investigated the evolutionary process of three Grammar-Guided GP (GGG...

Papers:

* Leon Ingelse, J. Ignacio Hidalgo, J. Manuel Colmenar, Nuno Lourenço & Alcides Fonseca, A comparison of representations in grammar-guided genetic programming in the context of glucose prediction in people with diabetes: link.springer.com/article/10.1...

GPEM journal has a new special issue on "twenty-five years of grammatical evolution"!

Edited and with an introduction by Mahdinejad, Murphy and Ryan.

Special issue: link.springer.com/collections/...

Introduction: link.springer.com/article/10.1...

Machine learning assisted evolutionary multi- and many-objective optimization by Saxena, et al. (review by Saltuk Buğra Selçuklu ) link.springer.com/article/10.1...

Artificial General Intelligence by Julian Togelius, (review by Vicente Martin Mastrocola) link.springer.com/article/10.1...
Symbolic Regression by Kronberg et al., (review by Bill La Cava ) link.springer.com/article/10.1...

Automatic Quantum Computer Programming: A Genetic Programming Approach by Lee Spector (review by Michel Toulouse), link.springer.com/article/10.1...

Ant Colony Optimizaton by Dorigo and Stutzle (review by Katya Rodríguez Vázquez) link.springer.com/article/10.1...

Evolutionary Robotics by Nolfi and Floreano, (review by Takashi Gomi) link.springer.com/article/10.1...

Foundations of Genetic Programming by Langdon and Poli, (review by Richard J. Povinelli) link.springer.com/article/10.1...

A lot of book reviews in GPEM Journal, old and new, which are now fully open access!

A review of “Symbolic Regression” by Gabriel Kronberger, Bogdan Burlacu, Michael Kommenda, Stephan M. Winkler, and Michael Affenzeller, ISBN 978-1-138-05481-3, 2024, CRC Press. - Genetic Programming a... Genetic Programming and Evolvable Machines -

New book review at GPEM:

Book: "Symbolic Regression" by Kronberger et al

Review by La Cava

link.springer.com/article/10.1...

#geneticprogramming

Lexicase Selection Parameter Analysis: Varying Population Size and Test Case Redundancy with Diagnostic Metrics Lexicase selectionLexicase selection is a successful parent selectionParent selection method in genetic programming that has outperformed other methods across multiple benchmark suitesBenchm...

Our GPTP from last year is out! Lexicase Selection Parameter Analysis: Varying Population Size and Test Case Redundancy with Diagnostic Metrics link.springer.com/chapter/10.1... #geneticprogramming

Editorial introduction to the special issue for the tenth anniversary of geometric semantic genetic programming - Genetic Programming and Evolvable Machines Genetic Programming and Evolvable Machines -

Editorial introduction by Moraglio et al: link.springer.com/article/10.1...

Geometric Semantic #geneticprogramming was a big breakthrough in GP in 2012. The relationship between syntax and semantics is - in one way - easy to understand and take advantage of. 10 years later (!), here is the GPEM special issue.

Special issue collection: link.springer.com/collections/...

RSCID: requirements selection considering interactions and dependencies - Genetic Programming and Evolvable Machines Requirements selection is one of the essential aspects of requirement engineering. So far, a lot of work has been done in this field. But, it is difficult to choose the right set of software requireme...

New paper in GPEM on requirements engineering:

"RSCID: requirements selection considering interactions and dependencies", by Keyvanpour et al.

link.springer.com/article/10.1...

#geneticprogramming

Now that you've finished CEC revisions... and finalising EuroGP camera-ready.. and you have GECCO acceptance decisions... and you've finished GECCO workshop submissions...

...keep up the momentum to get your paper ready for a GPEM submission!

#geneticprogramming

New book review in GPEM!

Book: The science of soft robots, Suzumori et al.

Review by: Medvet & Salvato

link.springer.com/article/10.1...

@ericmedvetts.bsky.social

Constraining genetic symbolic regression via semantic backpropagation - Genetic Programming and Evolvable Machines Evolutionary symbolic regression approaches are powerful tools that can approximate an explicit mapping between input features and observation for various problems. However, ensuring that explored exp...

"Constraining genetic symbolic regression via semantic backpropagation" by Reissman et al in GPEM

#geneticprogramming

link.springer.com/article/10.1...

New book review at GPEM

link.springer.com/article/10.1...

#geneticprogramming

Editorial introduction for the special issue on highlights of genetic programming 2023 events - Genetic Programming and Evolvable Machines Genetic Programming and Evolvable Machines -

The special issue on highlights of 2023 #geneticprogramming events, edited by Pappa, Giacobini, Ting Hu, and Jakobović is out:

link.springer.com/article/10.1...

GECCO paper reviews are due in 1 hour!

(poster reviews are due this time next week)

Memetic semantic boosting for symbolic regression - Genetic Programming and Evolvable Machines This paper introduces a novel approach called semantic boosting regression (SBR), leveraging the principles of boosting algorithms in symbolic regression using a Memetic Semantic GP for Symbolic Regre...

New paper in GPEM: "Memetic semantic boosting for symbolic regression"

Leite & Schoenauer

link.springer.com/article/10.1...

#geneticprogramming

New feature! PySR v1.4 lets you define a template expression to optimize that both has learnable parameters AND learnable expressions:

We're excited to announce the first Evolving Self-organisation workshop at GECCO 2025!

Submission deadline: March 26, 2025

More information: evolving-self-organisation-workshop.github.io