Results 1 to 10 of about 9,566,413 (315)
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
doaj +2 more sources
Differentiable Genetic Programming [PDF]
European Conference on Genetic Programming, 2016 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 ...
Biscani, Francesco +2 more
core +2 more sources
TensorFlow Enabled Genetic Programming [PDF]
Annual Conference on Genetic and Evolutionary Computation, 2017 Genetic Programming, a kind of evolutionary computation and machine learning algorithm, is shown to benefit significantly from the application of vectorized data and the TensorFlow numerical computation library on both CPU and GPU architectures. The open
Aniyan, Arun +4 more
core +2 more sources
Semantic variation operators for multidimensional genetic programming. [PDF]
Genet Evol Comput Conf, 2019 Multidimensional genetic programming represents candidate solutions as sets of programs, and thereby provides an interesting framework for exploiting building block identification. Towards this goal, we investigate the use of machine learning as a way to
La Cava W, Moore JH.
europepmc +2 more sources
Learning feature spaces for regression with genetic programming. [PDF]
Genet Program Evolvable Mach, 2020 La Cava W, Moore JH.
europepmc +2 more sources
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2020 This book constitutes the refereed proceedings of the 23rd European Conference on Genetic Programming, EuroGP 2020, held as part of Evo*2020, in Seville, Spain, in April 2020, co-located with the Evo*2020 events EvoCOP, EvoMUSART and EvoApplications. The 12 full papers and 6 short papers presented in this book were carefully reviewed and selected from ...
Una-May O'Reilly, Erik Hemberg
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Taylor genetic programming for symbolic regression [PDF]
Annual Conference on Genetic and Evolutionary Computation, 2022 Genetic programming (GP) is a commonly used approach to solve symbolic regression (SR) problems. Compared with the machine learning or deep learning methods that depend on the pre-defined model and the training dataset for solving SR problems, GP is more
Baihe He +4 more
semanticscholar +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 +2 more sources
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