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 2020-10-01 to 2024-10-01.)
ArticleCitations
An enhanced Huffman-PSO based image optimization algorithm for image steganography41
Graph representations in genetic programming15
TPOT-NN: augmenting tree-based automated machine learning with neural network estimators14
Genetic programming convergence13
Evolutionary approximation and neural architecture search12
Applying genetic programming to PSB2: the next generation program synthesis benchmark suite11
Evolving hierarchical memory-prediction machines in multi-task reinforcement learning10
Blood glucose prediction using multi-objective grammatical evolution: analysis of the “agnostic” and “what-if” scenarios9
Interpretability in symbolic regression: a benchmark of explanatory methods using the Feynman data set8
Semantically-oriented mutation operator in cartesian genetic programming for evolutionary circuit design7
Tag-based regulation of modules in genetic programming improves context-dependent problem solving5
Evolving continuous optimisers from scratch5
Constant optimization and feature standardization in multiobjective genetic programming5
Complexity and aesthetics in generative and evolutionary art5
Severe damage recovery in evolving soft robots through differentiable programming5
Symbolic-regression boosting4
Experiments in evolutionary image enhancement with ELAINE4
Evolutionary algorithms for designing reversible cellular automata4
Feature extraction by grammatical evolution for one-class time series classification3
GP-DMD: a genetic programming variant with dynamic management of diversity3
Alleviating overfitting in transformation-interaction-rational symbolic regression with multi-objective optimization3
Virginia Dignum: Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way3
A grammar-based GP approach applied to the design of deep neural networks3
Software review: Pony GE23
EvoStencils: a grammar-based genetic programming approach for constructing efficient geometric multigrid methods3
A semantic genetic programming framework based on dynamic targets3
On the performance of the Bayesian optimization algorithm with combined scenarios of search algorithms and scoring metrics3
A comparison of an evolvable hardware controller with an artificial neural network used for evolving the gait of a hexapod robot3
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