GPEM journal

GPEM journal

@gpem.bsky.social

Genetic Programming and Evolvable Machines journal https://link.springer.com/journal/10710 Editor-in-chief Leonardo Trujillo bsky feed maintained by James McDermott

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GPEM journal
@gpem.bsky.social

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...

link.springer.comOn fitting numerical features into probabilistic distributions to represent data for fuzzy pattern trees - Genetic Programming and Evolvable MachinesFuzzy 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.
GPEM journal
@gpem.bsky.social

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...

link.springer.comIntroducing look-ahead into relocation rules generated with genetic programming for the container relocation problem - Genetic Programming and Evolvable MachinesThe 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.
GPEM journal
@gpem.bsky.social

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...

link.springer.comQuality-diversity in problems with composite solutions: a case study on body–brain robot optimization - Genetic Programming and Evolvable MachinesWhen 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.
GPEM journal
@gpem.bsky.social

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/...

gpemjournal.blogspot.comCall for Papers: Special Issue on Generative AI and Evolutionary Computation for Software EngineeringSpecial Issue Home: https://link.springer.com/collections/bcadcgjdjd Generative models, and mainly large language models, are already wide...
GPEM journal
@gpem.bsky.social

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

link.springer.comAn investigation into structured grammatical evolution initialisation - Genetic Programming and Evolvable MachinesA 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,...
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GPEM journal reposted

Jason Moore
@moorejh.bsky.social

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

link.springer.comLexicase Selection Parameter Analysis: Varying Population Size and Test Case Redundancy with Diagnostic MetricsLexicase selectionLexicase selection is a successful parent selectionParent selection method in genetic programming that has outperformed other methods across multiple benchmark suitesBenchm...
GPEM journal
@gpem.bsky.social

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/...

link.springer.comSpecial Issue for the Tenth Anniversary of Geometric Semantic Genetic ProgrammingCall for Papers: https://www.springer.com/journal/10710/updates/23957712
GPEM journal
@gpem.bsky.social

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

link.springer.comRSCID: requirements selection considering interactions and dependencies - Genetic Programming and Evolvable MachinesRequirements 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...
GPEM journal
@gpem.bsky.social

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

GPEM journal
@gpem.bsky.social

"Constraining genetic symbolic regression via semantic backpropagation" by Reissman et al in GPEM #geneticprogramming link.springer.com/article/10.1...

link.springer.comConstraining genetic symbolic regression via semantic backpropagation - Genetic Programming and Evolvable MachinesEvolutionary 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...
GPEM journal
@gpem.bsky.social

From Crary et al, a new article in GPEM: Using FPGA devices to accelerate the evaluation phase of tree-based genetic programming: an extended analysis link.springer.com/article/10.1...

link.springer.comUsing FPGA devices to accelerate the evaluation phase of tree-based genetic programming: an extended analysis - Genetic Programming and Evolvable MachinesThis paper establishes the potential of accelerating the evaluation phase of tree-based genetic programming through contemporary field-programmable gate array (FPGA) technology. This exploration stems...
GPEM journal
@gpem.bsky.social

Check out this conference: www.maeb2025.org XVI Congreso Español de Metaheurísticas, Algoritmos Evolutivos y Bioinspirados May 2025, Donostia-San Sebastián, Spain Submission deadline 22 February.

maeb2025.orgXVI Congreso Español de Metaheurísticas, Algoritmos Evolutivos y BioinspiradosEl XVI Congreso Español de Metaheurística, Algoritmos Evolutivos y Bioinspirados (MAEB), pretende ser un foro de encuentro, discusión y transferencia de conocimientos entre investigadores en el campo ...
GPEM journal
@gpem.bsky.social

Another new paper in GPEM! 'El Nino' weather events, characterised with equations, by Abdulkarimova et al. link.springer.com/article/10.1... #geneticprogramming

link.springer.comHarnessing evolutionary algorithms for enhanced characterization of ENSO events - Genetic Programming and Evolvable MachinesThe El Niño-Southern Oscillation (ENSO) significantly influences the complexity and variability of the global climate system, driving its variability. ENSO events’ irregularity and unpredictability ar...

GPEM journal reposted

James McDermott
@jmmcd.bsky.social

Are there any #geneticprogramming people here? I'm wondering if GPEM should be on here as well as / instead of on Twitter. Same question for LinkedIn, Mastodon, Instagram.

GPEM journal reposted

Jason Moore
@moorejh.bsky.social

A Comparison of Large Language Models and Genetic Programming for Program Synthesis ieeexplore.ieee.org/abstract/doc... #llms #geneticprogramming #coding

ieeexplore.ieee.orgA Comparison of Large Language Models and Genetic Programming for Program SynthesisLarge language models have recently become known for their ability to generate computer programs, especially through tools such as GitHub Copilot, a domain where genetic programming has been very succ...

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