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 2021-06-01 to 2025-06-01.)
ArticleCitations
Introducing Design Automation for Quantum Computing, Alwin Zulehner and Robert Wille. ISBN 978-3-030-41753-6, 2020, Springer International Publishing. 222 Pages, 51 b/w illustrations, 14 illustrations20
A new hybrid method of Evolutionary-Numerical algorithms to solve ODEs arising in physics and engineering18
Evolutionary design and analysis of ribozyme-based logic gates15
A comparison of an evolvable hardware controller with an artificial neural network used for evolving the gait of a hexapod robot15
Geometric semantic genetic programming with normalized and standardized random programs13
Semantically-oriented mutation operator in cartesian genetic programming for evolutionary circuit design12
A new representation in 3D VLSI floorplan: 3D O-Tree11
A genetic algorithm for rule extraction in fuzzy adaptive learning control networks10
Severe damage recovery in evolving soft robots through differentiable programming9
An investigation into structured grammatical evolution initialisation8
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.8
GSGP-hardware: instantaneous symbolic regression with an FPGA implementation of geometric semantic genetic programming8
A semantic genetic programming framework based on dynamic targets8
Julian Togelius: Artificial General Intelligence, The MIT Press Essential Knowledge series, 2024, paperback, 230 pages, ISBN:97802625493496
Semantic segmentation network stacking with genetic programming5
Interpretability in symbolic regression: a benchmark of explanatory methods using the Feynman data set5
Evolutionary combination of connected event schemas into meaningful plots5
Semantic mutation operator for a fast and efficient design of bent Boolean functions5
A survey on dynamic populations in bio-inspired algorithms5
Hierarchical non-dominated sort: analysis and improvement5
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 analysis4
Highlights of genetic programming 2020 events4
A genetic programming approach to the automated design of CNN models for image classification and video shorts creation4
Evolutionary design of swing-up controllers for stabilization task of underactuated inverted pendulums4
“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
Relationships between parent selection methods, looping constructs, and success rate in genetic programming4
New directions in fitness evaluation: commentary on Langdon’s JAWS303
A novel tree-based representation for evolving analog circuits and its application to memristor-based pulse generation circuit3
Reward tampering and evolutionary computation: a study of concrete AI-safety problems using evolutionary algorithms3
On the performance of the Bayesian optimization algorithm with combined scenarios of search algorithms and scoring metrics3
RSCID: requirements selection considering interactions and dependencies3
The science of soft robots, Koichi Suzumori, Kenjiro Fukuda, Ryuma Niiyama, and Kohei Nakajima: ISBN 978-9811951732, Springer 20233
Evolving continuous optimisers from scratch3
A survey on batch training in genetic programming3
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