Results 11 to 20 of about 9,566,413 (315)
Genetic programming for production scheduling: a survey with a unified framework
Complex & Intelligent Systems, 2017 Genetic programming has been a powerful technique for automated design of production scheduling heuristics. Many studies have shown that heuristics evolved by genetic programming can outperform many existing heuristics manually designed in the literature.
Su Nguyen, Yi Mei, Mengjie Zhang
semanticscholar +3 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
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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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Graph representations in genetic programming
Genetic Programming and Evolvable Machines, 2021 Graph representations promise several desirable properties for genetic programming (GP); multiple-output programs, natural representations of code reuse and, in many cases, an innate mechanism for neutral drift. Each graph GP technique provides a program
Léo Françoso Dal Piccol Sotto +4 more
semanticscholar +1 more source
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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A genetic programming approach to designing convolutional neural network architectures [PDF]
Annual Conference on Genetic and Evolutionary Computation, 2017 The convolutional neural network (CNN), which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures still requires expert knowledge and a lot of trial and error.
M. Suganuma, S. Shirakawa, T. Nagao
semanticscholar +1 more source
Expressive Genetic Programming [PDF]
Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, 2016 The language in which evolving programs are expressed can have significant impacts on the dynamics and problem-solving capabilities of a genetic programming system. In GP these impacts are driven by far more than the absolute computational power of the languages used; just because a computation is theoretically possible in a language, it doesn't mean ...
Lee Spector, Nicholas Freitag McPhee
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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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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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