Genetic programming
Extracting Rules for Diagnosis of Diabetes Using Genetic Programming
... Methods: This study utilized the PIMA dataset of the university of California, Irvine. This dataset consists of the information of 768 Pima heritage women, including 500 healthy persons and 268 persons with diabetes. ...
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Infilling of Rainfall Information Using Genetic Programming
... of Genetic Programming (GP) based monthly model for infilling of missing rainfall records in the rainfall time series for 3 rain gauge stations in the Yarra River Basin in Australia from the available ...
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A Combined Genetic Programming for Microarray Data Analysis
... Meta-genetic programming is used to create rules and then applied to training samples to evaluate their fitness. Based on fitness, best rules are selected and those rules are applied to the test samples to ...
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PREDICTION OF ALGAL BLOOM USING GENETIC PROGRAMMING
... The primitives of GP, the function and terminal nodes, must be assembled into a structure before they may be executed. Three main types of structure exist: tree, linear and graph. In this study, the method utilized for ...
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PolyGP: a polymorphic genetic programming system in Haskell
... Automatic programming relies on additional knowledge about the problem solution to guide code synthesis and transformation [Lenat, ...1984]. Genetic Programming (GP) [Koza, 1992] automatically ...
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Defining Locality in Genetic Programming to Predict Performance
... Abstract— A key indicator of problem difficulty in evo- lutionary computation problems is the landscape’s locality, that is whether the genotype-phenotype mapping preserves neighbourhood. In genetic ...
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Evolving text classification rules with genetic programming
... 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 ...
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Wiener Model Identification using Genetic Programming
... evolutionary computation approach with piecewise linearization ,” Systems, Man, and Cybernetics, IEEE International Conference Volume. 3, 7-10 Digital Object Identifier 10.1109/ICSMC.2001.973479., vol.3, Oct. 2001, ...
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An Analysis of the Impact of Functional Programming Techniques on Genetic Programming
... The Genetic Programming (GP) [Koza, 1989, 1990, 1992, 1994a] paradigm is a problem solving method based on a computational analogy to natural ...by genetic operations o f crossover and mutation to ...
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Meta genetic programming for static quantum circuits
... automatic programming system in the first ...linear genetic programming [7], which creates circuits of the form seen in the ...function, genetic mutation, and genetic crossover meth- ...
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Data Cleaning by Genetic Programming Technique
... Genetic Programming is one of the evolutionary programming technique which having the properties of natural ...distinguish genetic programming from other evolutionary technique is that ...
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Network intrusion detection using genetic programming.
... Song et al. [83] applied linear genetic programming for network intrusion detection. The KDD-99 dataset was used to evaluate the performance of the classifier. Linear genetic programming was ...
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A Genetic Programming Approach to Automated Software Repair
... Genetic programming is combined with program analysis methods to repair bugs in off-the-shelf legacy C ...(3) genetic operators use exist- ing code in other parts of the program, so new code does not ...
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Revisiting the Sequential Symbolic Regression Genetic Programming
... Since the introduction of Genetic Programming (GP) [3], researchers have been interested in exploring the regularities and modularity of its search space [4]. One of the main motivations to identify these ...
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Cartesian Genetic Programming for Trading: A Preliminary Investigation
... Cartesian Genetic Programming (CGP) (Miller, ...tree-based Genetic Programming methods have no limit on size and the problems of bloat are well ...
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Learning Model-Ensemble Policies with Genetic Programming
... Abstract. We propose to apply typed Genetic Programming (GP) to the problem of finding surrogate-model ensembles for global optimization on compute-intensive target functions. In a model ensemble, ...
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Prediction of expected performance for a genetic programming classifier
... In this work we derive PEPs for the probabilistic genetic programming classifier (PGPC) proposed in [75], a feature construction method. PGPC was chosen due to its simplicity and strong performance on ...
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PolyGP: A Polymorphic Genetic Programming System in Haskell
... In general, the machine learning process can be accelerated through the use of heuristic knowledge about the problem solution. For example, monomorphic typed Genetic Programming (GP) uses type information ...
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Realised Volatility Forecasting: A Genetic Programming Approach
... Genetic Programming’s (GP) model induction capability has been previously applied for volatility modelling and has achieved good results [5], [7], [8], [9] and [11]. However, there are still some important ...
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Genetic Programming Algorithm for Designing of Control Systems
... the genetic algorithm (GA) (used for numeric parameter tuning) and genetic programming (GP) (used for parameter tuning and structure selec- ...
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