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1,493
Lineargraph gp  a new gp structure
 Genetic Programming, Proceedings of EuroGP'2002, LNCS
, 2002
"... Abstract. In recent years different genetic programming (GP) structures have emerged. Today, the basic forms of representation for genetic programs are tree, linear and graphstructures. In this contribution we introduce a new kind of GP structure which we call lineargraph. This is a further develop ..."
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Cited by 11 (2 self)
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Abstract. In recent years different genetic programming (GP) structures have emerged. Today, the basic forms of representation for genetic programs are tree, linear and graphstructures. In this contribution we introduce a new kind of GP structure which we call lineargraph. This is a further
LogGP: Incorporating Long Messages into the LogP Model  One step closer towards a realistic model for parallel computation
, 1995
"... We present a new model of parallel computationthe LogGP modeland use it to analyze a number of algorithms, most notably, the single node scatter (onetoall personalized broadcast). The LogGP model is an extension of the LogP model for parallel computation [CKP + 93] which abstracts the comm ..."
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Cited by 287 (1 self)
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We present a new model of parallel computationthe LogGP modeland use it to analyze a number of algorithms, most notably, the single node scatter (onetoall personalized broadcast). The LogGP model is an extension of the LogP model for parallel computation [CKP + 93] which abstracts
A New GPevolved Formulation for the Relative Permittivity of Water and Steam
"... The relative permittivity (or static dielectric constant) of water and steam has been experimentally calculated at a relatively wide range of temperatures and pressures. A single function for predicting the relative permittivity of water and steam in three distinct thermodynamic regions is evolved u ..."
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The relative permittivity (or static dielectric constant) of water and steam has been experimentally calculated at a relatively wide range of temperatures and pressures. A single function for predicting the relative permittivity of water and steam in three distinct thermodynamic regions is evolved using genetic programming. A data set comprised of all of the most accurate relative permittivity values, along with temperature, pressure, and density values from the entire experimentally calculated range of these values, found in [Fern95], is used for this task. The accuracy of this function is evaluated by comparing the values for the relative permittivity calculated using the evolved function and the values calculated using the latest formulation of Fernandez et al., found in [Fern97] to the aforementioned data set. In all regions, the newly evolved function outperforms the most current formulation in terms of difference between calculated and experimentally obtained values for the dielectric constant.
Sparse Gaussian processes using pseudoinputs
 Advances in Neural Information Processing Systems 18
, 2006
"... We present a new Gaussian process (GP) regression model whose covariance is parameterized by the the locations of M pseudoinput points, which we learn by a gradient based optimization. We take M ≪ N, where N is the number of real data points, and hence obtain a sparse regression method which has O( ..."
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Cited by 229 (13 self)
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We present a new Gaussian process (GP) regression model whose covariance is parameterized by the the locations of M pseudoinput points, which we learn by a gradient based optimization. We take M ≪ N, where N is the number of real data points, and hence obtain a sparse regression method which has O
Lineartree GP and its comparison with other GP structures
 Genetic Programming, Proceedings of EuroGP’2001, volume 2038 of LNCS
, 2001
"... Abstract. In recent years different genetic programming (GP) structures have emerged. Today, the basic forms of representation for genetic programs are tree, linear and graph structures. In this contribution we introduce a new kind of GP structure which we call Lineartree. We describe the lineartr ..."
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Cited by 16 (5 self)
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Abstract. In recent years different genetic programming (GP) structures have emerged. Today, the basic forms of representation for genetic programs are tree, linear and graph structures. In this contribution we introduce a new kind of GP structure which we call Lineartree. We describe the linear
An Experimental Analysis of Schema Creation, Propagation and Disruption in Genetic Programming
, 1997
"... In this paper we first review the main results in the theory of schemata in Genetic Programming (GP) and summarise a new GP schema theory which is based on a new definition of schema. ..."
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Cited by 21 (13 self)
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In this paper we first review the main results in the theory of schemata in Genetic Programming (GP) and summarise a new GP schema theory which is based on a new definition of schema.
The Design of GP 2
, 2012
"... This papers defines the syntax and semantics of GP 2, a revised version of the graph programming language GP. New concepts are illustrated and explained with example programs. Changes to the first version of GP include an improved type system for labels, a builtin marking mechanism for nodes and ed ..."
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Cited by 7 (6 self)
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This papers defines the syntax and semantics of GP 2, a revised version of the graph programming language GP. New concepts are illustrated and explained with example programs. Changes to the first version of GP include an improved type system for labels, a builtin marking mechanism for nodes
Hyperschema Theory for GP with OnePoint Crossover, Building Blocks, and Some New Results in GA Theory
 Genetic Programming, Proceedings of EuroGP 2000
, 2000
"... Two main weaknesses of GA and GP schema theorems axe that they provide only information on the expected value of the number of instances of a given schema at the next generation E[m(H,t + 1)], and they can only give a lower bound for such a quantity. This paper presents new theoretical results o ..."
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Cited by 29 (18 self)
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Two main weaknesses of GA and GP schema theorems axe that they provide only information on the expected value of the number of instances of a given schema at the next generation E[m(H,t + 1)], and they can only give a lower bound for such a quantity. This paper presents new theoretical results
ON FORBIDDEN MINORS FOR GP(3)
, 1988
"... A new, surprisingly simple proof is given of the finiteness of the set of matroids minorminimally not representable over GF(3). It is, in fact, proved that every such matroid has rank or corank at most 3. ..."
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A new, surprisingly simple proof is given of the finiteness of the set of matroids minorminimally not representable over GF(3). It is, in fact, proved that every such matroid has rank or corank at most 3.
Evaluating GP schema in context
 In GECCO 2005: Proceedings of
, 2005
"... We propose a new methodology to look at the fitness contributions (semantics) of different schemata in Genetic Programming (GP). We hypothesize that the significance of a schema can be evaluated by calculating its fitness contribution to the total fitness of the trees that contain it, and use our me ..."
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Cited by 1 (0 self)
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We propose a new methodology to look at the fitness contributions (semantics) of different schemata in Genetic Programming (GP). We hypothesize that the significance of a schema can be evaluated by calculating its fitness contribution to the total fitness of the trees that contain it, and use our
Results 1  10
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1,493