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 2021-09-01 to 2025-09-01.)
ArticleCitations
A new hybrid method of Evolutionary-Numerical algorithms to solve ODEs arising in physics and engineering21
Evolutionary design and analysis of ribozyme-based logic gates16
A comparison of an evolvable hardware controller with an artificial neural network used for evolving the gait of a hexapod robot16
Semantically-oriented mutation operator in cartesian genetic programming for evolutionary circuit design15
On fitting numerical features into probabilistic distributions to represent data for fuzzy pattern trees15
Geometric semantic genetic programming with normalized and standardized random programs12
Severe damage recovery in evolving soft robots through differentiable programming10
A genetic algorithm for rule extraction in fuzzy adaptive learning control networks10
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.9
A new representation in 3D VLSI floorplan: 3D O-Tree9
Julian Togelius: Artificial General Intelligence, The MIT Press Essential Knowledge series, 2024, paperback, 230 pages, ISBN:97802625493498
An investigation into structured grammatical evolution initialisation8
A semantic genetic programming framework based on dynamic targets7
GSGP-hardware: instantaneous symbolic regression with an FPGA implementation of geometric semantic genetic programming7
Semantic mutation operator for a fast and efficient design of bent Boolean functions6
Hierarchical non-dominated sort: analysis and improvement6
Interpretability in symbolic regression: a benchmark of explanatory methods using the Feynman data set6
Evolutionary combination of connected event schemas into meaningful plots5
“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, 20245
Semantic segmentation network stacking with genetic programming5
A genetic programming approach to the automated design of CNN models for image classification and video shorts creation4
A survey on dynamic populations in bio-inspired algorithms4
Highlights of genetic programming 2020 events4
An oversampling method based on adaptive artificial immune network and SMOTE4
Evolutionary design of swing-up controllers for stabilization task of underactuated inverted pendulums4
A comparison of representations in grammar-guided genetic programming in the context of glucose prediction in people with diabetes4
Using FPGA devices to accelerate the evaluation phase of tree-based genetic programming: an extended analysis3
On the performance of the Bayesian optimization algorithm with combined scenarios of search algorithms and scoring metrics3
A survey on batch training in genetic programming3
Evolving continuous optimisers from scratch3
Relationships between parent selection methods, looping constructs, and success rate in genetic programming3
RSCID: requirements selection considering interactions and dependencies3
A novel tree-based representation for evolving analog circuits and its application to memristor-based pulse generation circuit3
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