Results 31 to 40 of about 174,883 (339)
Tag-based regulation of modules in genetic programming improves context-dependent problem solving [PDF]
, 2020 We introduce and experimentally demonstrate the utility of tag-based genetic regulation, a new genetic programming (GP) technique that allows programs to dynamically adjust which code modules to express. Tags are evolvable labels that provide a flexible mechanism for referencing code modules.
arxiv +1 more source
Genetic Programming-Based Machine Degradation Modeling Methodology
IEEE Open Journal of Instrumentation and Measurement, 2022 Machine degradation is a complex, dynamic and irreversible process and its modeling is a leading-edge technology in prognostics and health management (PHM).
Tongtong Yan, Dong Wang
doaj +1 more source
Memetic Semantic Genetic Programming [PDF]
Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2015 Semantic Backpropagation (SB) was introduced in GP so as to take into account the semantics of a GP tree at all intermediate states of the program execution, i.e., at each node of the tree. The idea is to compute the optimal " should-be " values each subtree should return, whilst assuming that the rest of the tree is unchanged, so as to minimize the ...
Ffrancon, Robyn, Schoenauer, Marc
openaire +5 more sources
Revisiting Genetic Network Programming (GNP): Towards the Simplified Genetic Operators
IEEE Access, 2018 Genetic network programming (GNP) is a relatively new type of graph-based evolutionary algorithm, which designs a directed graph structure for its individual representation.
Xianneng Li, Huiyan Yang, Meihua Yang
doaj +1 more source
Recent Patents on Genetic Programming [PDF]
Recent Patents on Computer Sciencee, 2009 Genetic Programming is a form of Natural Computing which adopts principles from neo-Darwinian evolution to automatically solve problems. It is a model induction method in that both the structure and parameters of the solution are explored simultaneously.
O'Neill, Michael, Brabazon, Anthony
openaire +4 more sources
On Comprehension of Genetic Programming Solutions: A Controlled Experiment on Semantic Inference
Mathematics, 2022 Applied to the problem of automatic program generation, Genetic Programming often produces code bloat, or unexpected solutions that are, according to common belief, difficult to comprehend.
Boštjan Slivnik +3 more
doaj +1 more source
Genetic Programming for Manifold Learning: Preserving Local Topology [PDF]
, 2021 Manifold learning methods are an invaluable tool in today's world of increasingly huge datasets. Manifold learning algorithms can discover a much lower-dimensional representation (embedding) of a high-dimensional dataset through non-linear transformations that preserve the most important structure of the original data.
arxiv +1 more source
A Genetic Programming-Driven Data Fitting Method
IEEE Access, 2020 Data fitting is the process of constructing a curve, or a set of mathematical functions, that has the best fit to a series of data points. Different with constructing a fitting model from same type of function, such as the polynomial model, we notice ...
Hao Chen +3 more
doaj +1 more source
Evolutionary Algorithms in a Bacterial Consortium of Synthetic Bacteria
Algorithms, 2023 At present, synthetic biology applications are based on the programming of synthetic bacteria with custom-designed genetic circuits through the application of a top-down strategy. These genetic circuits are the programs that implement a certain algorithm,
Sara Lledó Villaescusa +1 more
doaj +1 more source
FEBS Letters, EarlyView.In vivo IL‐10 produced by tissue‐resident tolDC is involved in maintaining/inducing tolerance. Depending on the agent used for ex vivo tolDC generation, cells acquire common features but prime T cells towards anergy, FOXP3+ Tregs, or Tr1 cells according to the levels of IL‐10 produced. Ex vivo‐induced tolDC were administered to patients to re‐establish/
Konstantina Morali +3 more
wiley +1 more source