Results 21 to 30 of about 174,883 (339)
PENDEKATAN ALGORITMA GENETIKA DALAM MENYELESAIKAN PERMASALAHAN FUZZY LINEAR PROGRAMMING
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2011 Fuzzy linear programming is one of the linear programming developments which able to accommodate uncertainty in the real world. Genetic algorithm approach in solving linear programming problems with fuzzy constraints has been introduced by Lin (2008) by ...
Siska Dewi Lestari, Subanar Subanar
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Differentiable Genetic Programming [PDF]
, 2017 We introduce the use of high order automatic differentiation, implemented via the algebra of truncated Taylor polynomials, in genetic programming. Using the Cartesian Genetic Programming encoding we obtain a high-order Taylor representation of the program output that is then used to back-propagate errors during learning.
Dario Izzo +2 more
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The Train Delay Model Developed by the Genetic Programming Algorithm
Journal of Advanced Transportation, 2022 The paper discusses the problem of probability distribution category identification of train delay data by a genetic programming algorithm. This train delay frequency function and the probability distribution simply derived from it are significant to ...
Tomas Brandejsky
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Genetic programming convergence
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2021 We study both genotypic and phenotypic convergence in GP floating point continuous domain symbolic regression over thousands of generations. Subtree fitness variation across the population is measured and shown in many cases to fall. In an expanding region about the root node, both genetic opcodes and function evaluation values are identical or nearly ...
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An investigation into the application of genetic programming to combinatorial game theory [PDF]
arXiv, 2021 Genetic programming is the practice of evolving formulas using crossover and mutation of genes representing functional operations. Motivated by genetic evolution we develop and solve two combinatorial games, and we demonstrate some advantages and pitfalls of using genetic programming to investigate Grundy values.
arxiv
Forecasting Shaharchay River Flow in Lake Urmia Basin using Genetic Programming and M5 Model Tree
مجله آب و خاک, 2017 Introduction: Precise prediction of river flows is the key factor for proper planning and management of water resources. Thus, obtaining the reliable methods for predicting river flows has great importance in water resource engineering.
S. Samadianfard, R. Delirhasannia
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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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Choose Your Programming Copilot: A Comparison of the Program Synthesis Performance of GitHub Copilot and Genetic Programming [PDF]
arXiv, 2021 GitHub Copilot, an extension for the Visual Studio Code development environment powered by the large-scale language model Codex, makes automatic program synthesis available for software developers. This model has been extensively studied in the field of deep learning, however, a comparison to genetic programming, which is also known for its performance
arxiv
Bias-variance decomposition in Genetic Programming
Open Mathematics, 2016 We study properties of Linear Genetic Programming (LGP) through several regression and classification benchmarks. In each problem, we decompose the results into bias and variance components, and explore the effect of varying certain key parameters on the
Kowaliw Taras, Doursat René
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Does your gene need a background check? How genetic background impacts the analysis of mutations, genes, and evolution [PDF]
Trends in Genetics 2013 29(6):358-366, 2013 The premise of genetic analysis is that a causal link exists between phenotypic and allelic variation. Yet it has long been documented that mutant phenotypes are not a simple result of a single DNA lesion, but rather are due to interactions of the focal allele with other genes and the environment.
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