Genetic Programming and Evolvable Machines

Papers
(The TQCC of Genetic Programming and Evolvable Machines is 3. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2021-11-01 to 2025-11-01.)
ArticleCitations
A new hybrid method of Evolutionary-Numerical algorithms to solve ODEs arising in physics and engineering21
A comparison of an evolvable hardware controller with an artificial neural network used for evolving the gait of a hexapod robot17
On fitting numerical features into probabilistic distributions to represent data for fuzzy pattern trees16
Evolutionary design and analysis of ribozyme-based logic gates16
Geometric semantic genetic programming with normalized and standardized random programs13
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.11
A new representation in 3D VLSI floorplan: 3D O-Tree11
A genetic algorithm for rule extraction in fuzzy adaptive learning control networks10
An investigation into structured grammatical evolution initialisation9
Severe damage recovery in evolving soft robots through differentiable programming9
Julian Togelius: Artificial General Intelligence, The MIT Press Essential Knowledge series, 2024, paperback, 230 pages, ISBN:97802625493499
Semantic mutation operator for a fast and efficient design of bent Boolean functions8
GSGP-hardware: instantaneous symbolic regression with an FPGA implementation of geometric semantic genetic programming8
Interpretability in symbolic regression: a benchmark of explanatory methods using the Feynman data set7
Semantic segmentation network stacking with genetic programming5
Evolutionary combination of connected event schemas into meaningful plots5
An oversampling method based on adaptive artificial immune network and SMOTE4
A comparison of representations in grammar-guided genetic programming in the context of glucose prediction in people with diabetes4
Hierarchical non-dominated sort: analysis and improvement4
“Machine learning assisted evolutionary multi- and many-objective optimization” by Dhish Kumar Saxena, Sukrit Mittal, Kalyanmoy Deb, and Erik D. Goodman, ISBN 978-981-99-2095-2, Springer, 20244
Introducing look-ahead into relocation rules generated with genetic programming for the container relocation problem3
Using FPGA devices to accelerate the evaluation phase of tree-based genetic programming: an extended analysis3
Evolutionary design of swing-up controllers for stabilization task of underactuated inverted pendulums3
A genetic programming approach to the automated design of CNN models for image classification and video shorts creation3
A survey on batch training in genetic programming3
A survey on dynamic populations in bio-inspired algorithms3
RSCID: requirements selection considering interactions and dependencies3
0.061376094818115