Results 1 to 10 of about 102,518 (248)
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
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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
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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
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GSGP-CUDA — A CUDA framework for Geometric Semantic Genetic Programming
SoftwareX, 2022 Geometric Semantic Genetic Programming (GSGP) is a state-of-the-art machine learning method based on evolutionary computation. GSGP performs search operations directly at the level of program semantics, which can be done more efficiently than operating ...
Leonardo Trujillo +4 more
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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
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Genetic programming as alternative for predicting development effort of individual software projects. [PDF]
PLoS ONE, 2012 Statistical and genetic programming techniques have been used to predict the software development effort of large software projects. In this paper, a genetic programming model was used for predicting the effort required in individually developed projects.
Arturo Chavoya +3 more
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A Genetic Programming Approach to Binary Classification Problem [PDF]
EAI Endorsed Transactions on Energy Web, 2021 The Binary classification is the most challenging problem in machine learning. One of the most promising technique to solvethis problem is by implementing genetic programming (GP).
Leo Santoso +4 more
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From Requirements to Source Code: Evolution of Behavioral Programs
Applied Sciences, 2022 Automatically generating executable code has a long history of arguably modest success, mostly limited to the generation of small programs of up to 200 lines of code, and genetic improvement of existing code. We present the use of genetic programming (GP)
Roy Poliansky +2 more
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Scientific Reports, 2022 Niemann–Pick disease type C1 (NPC1) is a rare, prematurely fatal lysosomal storage disorder which exhibits highly variable severity and disease progression as well as a wide-ranging age of onset, from perinatal stages to adulthood. This heterogeneity has
Laura L. Baxter +8 more
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Route Stability in the Uncertain Capacitated Arc Routing Problem
Frontiers in Energy Research, 2022 Power line inspections in a microgrid can be modeled as the uncertain capacitated arc routing problem, which is a classic combinatorial optimization problem.
Yuxin Liu +3 more
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