Results 1 to 10 of about 174,784 (240)
Genetic Programming of Hypertension [PDF]
Frontiers in Pediatrics, 2018 The heritability of hypertension (HTN) is widely recognized and as a result, extensive studies ranging from genetic linkage analyses to genome-wide association studies are actively ongoing to elucidate the etiology of both monogenic and polygenic forms ...
Sun-Young Ahn +3 more
doaj +6 more sources
Code Building Genetic Programming [PDF]
Proceedings of the 2020 Genetic and Evolutionary Computation Conference, 2020 In recent years the field of genetic programming has made significant advances towards automatic programming. Research and development of contemporary program synthesis methods, such as PushGP and Grammar Guided Genetic Programming, can produce programs that solve problems typically assigned in introductory academic settings.
Edward Pantridge, Lee Spector
arxiv +4 more sources
Hematopoiesis: Progenitors and their genetic program [PDF]
Current Biology, 1998 Recent advances in our understanding of blood cell development have included the identification of a hematopoietic progenitor derived from endothelial cells and the possibility that the embryonic and fetal environment can reprogram gene expression in adult hematopoietic stem cells.
Françoise Dieterlen‐Lièvre
openalex +5 more sources
Multimodal spatiotemporal transcriptomic resolution of embryonic palate osteogenesis
Nature Communications, 2023 The terminal differentiation of osteoblasts and subsequent formation of bone marks an important phase in palate development that leads to the separation of the oral and nasal cavities.
Jeremie Oliver Piña +14 more
doaj +1 more source
IEEE Access, 2022 Dispatching rules are most commonly used to solve scheduling problems under dynamic conditions. Since designing new dispatching rules is a time-consuming process, it can be automated by using various machine learning and evolutionary computation methods.
Lucija Planinic +3 more
doaj +1 more source
Zoetrope genetic programming for regression [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference, 2021 The Zoetrope Genetic Programming (ZGP) algorithm is based on an original representation for mathematical expressions, targeting evolutionary symbolic regression.The zoetropic representation uses repeated fusion operations between partial expressions, starting from the terminal set.
Boisbunon, Aurélie +5 more
openaire +6 more sources
Liquid State Genetic Programming [PDF]
In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B.
(eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes
in Computer Science, vol 4431. Springer, 2023 A new Genetic Programming variant called Liquid State Genetic Programming (LSGP) is proposed in this paper. LSGP is a hybrid method combining a dynamic memory for storing the inputs (the liquid) and a Genetic Programming technique used for the problem solving part.
arxiv +1 more source
Genetic programming for multibiometrics [PDF]
Expert Systems with Applications, 2012 Biometric systems suffer from some drawbacks: a biometric system can provide in general good performances except with some individuals as its performance depends highly on the quality of the capture. One solution to solve some of these problems is to use multibiometrics where different biometric systems are combined together (multiple captures of the ...
Giot, Romain, Rosenberger, Christophe
openaire +6 more sources
Soft-Computing-Based Estimation of a Static Load for an Overhead Crane
Sensors, 2023 Payload weight detection plays an important role in condition monitoring and automation of cranes. Crane cells and scales are commonly used in industrial practice; however, when their installation to the hoisting equipment is not possible or costly, an ...
Tom Kusznir, Jaroslaw Smoczek
doaj +1 more source
Inclusive Genetic Programming [PDF]
, 2021 The promotion and maintenance of the population diversity in a Genetic Programming (GP) algorithm was proved to be an important part of the evolutionary process. Such diversity maintenance improves the exploration capabilities of the GP algorithm, which as a consequence improves the quality of the found solutions by avoiding local optima.
Francesco Marchetti, Edmondo Minisci
openaire +3 more sources