Results 11 to 20 of about 377,378 (213)
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.
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Fine-grained timing using genetic programming [PDF]
, 2010 In previous work, we have demonstrated that it is possible to use Genetic Programming to minimise the resource consumption of software, such as its power consumption or execution time. In this paper, we investigate the extent to which Genetic Programming
A.F. Webster +4 more
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Gene expression programming approach to event selection in high energy physics [PDF]
, 2006 Gene Expression Programming is a new evolutionary algorithm that overcomes many limitations of the more established Genetic Algorithms and Genetic Programming. Its first application to high energy physics data analysis is presented.
Teodorescu, L
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Event-based graphical monitoring in the EpochX genetic programming framework [PDF]
, 2013 EpochX is a genetic programming framework with provision for event management – similar to the Java event model – allowing the notification of particular actions during the lifecycle of the evolutionary algorithm. It also provides a flexible Stats system
Castle, Tom +3 more
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Differentiable 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
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Evolving text classification rules with genetic programming [PDF]
, 2005 We describe a novel method for using genetic programming to create compact classification rules using combinations of N-grams (character strings). Genetic programs acquire fitness by producing rules that are effective classifiers in terms of precision ...
Anthony N. +14 more
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TensorFlow Enabled Genetic Programming
, 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
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Genetic Programming for Multibiometrics
, 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
Giot, Romain, Rosenberger, Christophe
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Multitask Evolution with Cartesian Genetic Programming
, 2017 We introduce a genetic programming method for solving multiple Boolean circuit synthesis tasks simultaneously. This allows us to solve a set of elementary logic functions twice as easily as with a direct, single-task approach.Comment: 2 ...
Caruana Rich +2 more
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Pediatric Blood &Cancer, EarlyView.Abstract Background Sickle cell disease (SCD) is an autosomal recessive hemoglobinopathy affecting millions of individuals worldwide. The clinical expression and psychosocial burden of SCD vary widely across geographical, cultural, and healthcare system contexts, underscoring the need for setting‐specific approaches to assessment.
Desiré Fantasia +7 more
wiley +1 more source