Personalisation in smart phones requires adaptability to dynamic context based on user mobility, application usage and sensor inputs. Current personalisation approaches, which rely on static logic that is developed a ...
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Personalisation in smart phones requires adaptability to dynamic context based on user mobility, application usage and sensor inputs. Current personalisation approaches, which rely on static logic that is developed a priori, do not provide sufficient adaptability to dynamic and unexpected context. This paper proposes genetic programming (GP), which can evolve program logic in realtime, as an online learning method to deal with the highly dynamic context in smart phone personalisation. We introduce the concept of collaborative smart phone personalisation through the GP Island Model, in order to exploit shared context among co-located phone users and reduce convergence time. We implement these concepts on real smartphones to demonstrate the capability of personalisation through GP and to explore the benefits of the Island Model. Our empirical evaluations on two example applications confirm that the Island Model can reduce convergence time by up to two-thirds over standalone GP personalisation. Crown Copyright (C) 2014 Published by Elsevier B.V. All rights reserved.
Statistical methods, and in particular machine learning, have been increasingly used in the drug development workflow. Among the existing machine learning methods, we have been specifically concerned with genetic prog...
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Statistical methods, and in particular machine learning, have been increasingly used in the drug development workflow. Among the existing machine learning methods, we have been specifically concerned with genetic programming. We present a genetic programming-based framework for predicting anticancer therapeutic response. We use the NCI-60 microarray dataset and we look for a relationship between gene expressions and responses to oncology drugs Fluorouracil, Fludarabine, Floxuridine and Cytarabine. We aim at identifying, from genomic measurements of biopsies, the likelihood to develop drug resistance. Experimental results. and their comparison with the ones obtained by Linear Regression and Least Square Regression, hint that genetic programming is a promising technique for this kind of application. Moreover, genetic programming output may potentially highlight some relations between genes which could support the identification of biological meaningful pathways. The structures that appear more frequently in the "best" solutions found by genetic programming are presented. (C) 2009 Elsevier Ltd. All rights reserved.
The higher heating value (HHV) is the most important indicator of a coal's potential energy yield. It is commonly used in the efficiency and optimal design calculations pertaining to the coal combustion and gasifi...
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The higher heating value (HHV) is the most important indicator of a coal's potential energy yield. It is commonly used in the efficiency and optimal design calculations pertaining to the coal combustion and gasification processes. Since the experimental determination of coal's HHV is tedious and time-consuming, a number of proximate and/or ultimate analyses based correlations which are mostly linear have been proposed for its estimation. Owing to the fact that relationships between some of the constituents of the proximate/ultimate analyses and the HHV are nonlinear, the linear models make suboptimal predictions. Also, a majority of the currently available HHV models are restricted to the coals of specific ranks or particular geographical regions. Accordingly, in this study three proximate and ultimate analysis based nonlinear correlations have been developed for the prediction of HHV of coals by utilizing the computational intelligence (CI) based genetic programming (GP) formalism. Each of these correlations possesses following noteworthy characteristics: (i) the highest HHV prediction accuracy and generalization capability as compared to the existing models, (ii) wider applicability for coals of different ranks and from diverse geographies, and (iii) structurally lower complex than the other CI-based existing HHV models. It may also be noted that in this study, the GP technique has been used for the first time for developing coal specific HHV models. Owing to the stated attractive features, the GP-based models proposed here possess a significant potential to replace the existing models for predicting the HHV of coals. (C) 2016 Energy Institute. Published by Elsevier Ltd. All rights reserved.
genetic programming is the implementation of the paradigm of the survival of the fittest from the natural world in the world of computation. genetic programming is used to automatically create solutions to problems wh...
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genetic programming is the implementation of the paradigm of the survival of the fittest from the natural world in the world of computation. genetic programming is used to automatically create solutions to problems where the governing mechanisms are unknown. In this paper we apply genetic programming to the recognition of hand written digits from the USPS data set. To our knowledge there have been no results presented on this data set using genetic programming. We have introduced some variations on the selection and evolution methods normally used in genetic programming systems, in particular: aged members, directed crossover, inter-output crossover and node mutation. (C) 2004 Elsevier B.V. All rights reserved.
Image classification is a popular task in machine learning and computer vision, but it is very challenging due to high variation crossing images. Using ensemble methods for solving image classification can achieve hig...
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Image classification is a popular task in machine learning and computer vision, but it is very challenging due to high variation crossing images. Using ensemble methods for solving image classification can achieve higher classification performance than using a single classification algorithm. However, to obtain a good ensemble, the component (base) classifiers in an ensemble should be accurate and diverse. To solve image classification effectively, feature extraction is necessary to transform raw pixels into high-level informative features. However, this process often requires domain knowledge. This article proposes an evolutionary approach based on genetic programming to automatically and simultaneously learn informative features and evolve effective ensembles for image classification. The new approach takes raw images as inputs and returns predictions of class labels based on the evolved classifiers. To achieve this, a new individual representation, a new function set, and a new terminal set are developed to allow the new approach to effectively find the best solution. More important, the solutions of the new approach can extract informative features from raw images and can automatically address the diversity issue of the ensembles. In addition, the new approach can automatically select and optimize the parameters for the classification algorithms in the ensemble. The performance of the new approach is examined on 13 different image classification datasets of varying difficulty and compared with a large number of effective methods. The results show that the new approach achieves better classification accuracy on most datasets than the competitive methods. Further analysis demonstrates that the new approach can evolve solutions with high accuracy and diversity.
This paper investigates the use of genetic programming (GP) to create an approximate model for the non-linear relationship between flexural stiffness, length, mass per unit length and rotation speed associated with ro...
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This paper investigates the use of genetic programming (GP) to create an approximate model for the non-linear relationship between flexural stiffness, length, mass per unit length and rotation speed associated with rotating beams and their natural frequencies. GP, a relatively new form of artificial intelligence, is derived from the Darwinian concept of evolution and genetics and it creates computer programs to solve problems by manipulating their tree structures. GP predicts the size and structural complexity of the empirical model by minimizing the mean square error at the specified points of input-output relationship dataset. This dataset is generated using a finite element model. The validity of the GP-generated model is tested by comparing the natural frequencies at training and at additional input data points. It is found that by using a non-dimensional stiffness, it is possible to get simple and accurate function approximation for the natural frequency. This function approximation model is then used to study the relationships between natural frequency and various influencing parameters for uniform and tapered beams. The relations obtained with GP model agree well with FEM results and can be used for preliminary design and structural optimization studies.
Nowadays, object recognition based on local invariant features is widely acknowledged as one of the best paradigms for object recognition due to its robustness for solving image matching across different views of a gi...
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Nowadays, object recognition based on local invariant features is widely acknowledged as one of the best paradigms for object recognition due to its robustness for solving image matching across different views of a given scene. This paper proposes a new approach for learning invariant region descriptor operators through genetic programming and introduces another optimization method based on a hill-climbing algorithm with multiple re-starts. The approach relies on the synthesis of mathematical expressions that extract information derived from local image patches called local features. These local features have been previously designed by human experts using traditional representations that have a clear and, preferably mathematically, well-founded definition. We propose in this paper that the mathematical principles that are used in the description of such local features could be well optimized using a genetic programming paradigm. Experimental results confirm the validity of our approach using a widely accepted testbed that is used for testing local descriptor algorithms. In addition, we compare our results not only against three state-of-the-art algorithms designed by human experts, but also, against a simpler search method for automatically generating programs such as hill-climber. Furthermore, we provide results that illustrate the performance of our improved SIFT algorithms using an object recognition application for indoor and outdoor scenarios.
This research is aimed to develop new practical equations to predict flyrock distance based on genetic programming (GP) and genetic expression programming (GEP) techniques. For this purpose, 97 blasting operations in ...
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This research is aimed to develop new practical equations to predict flyrock distance based on genetic programming (GP) and genetic expression programming (GEP) techniques. For this purpose, 97 blasting operations in Delkan iron mine, Iran were investigated and the most effective parameters on flyrock were recorded. A database comprising of five inputs (i.e. burden, spacing, stemming length, hole depth, and powder factor) and one output (flyrock) was prepared to develop flyrock distance. Several GP and GEP models were proposed to predict flyrock considering the modeling procedures of them. To compare the performance prediction of the developed models, coefficient of determination (R-2), mean absolute error (MAE), root mean squared error (RMSE) and variance account for (VAF) were computed and then, the best GP and GEP models were selected. According to the obtained results, it was found that the best flyrock predictive model is the GEP based-model. As an example, considering results of RMSE, values of 2.119 and 2.511 for training and testing datasets of GEP model, respectively show higher accuracy of this model in predicting flyrock, while, these values were obtained as 5.788 and 10.062 for GP model. (C) 2016 Elsevier Ltd. All rights reserved.
An effective automated pulmonary nodule detection system can assist radiologists in detecting lung abnormalities at an early stage. In this paper, we propose a novel pulmonary nodule detection system based on a geneti...
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An effective automated pulmonary nodule detection system can assist radiologists in detecting lung abnormalities at an early stage. In this paper, we propose a novel pulmonary nodule detection system based on a genetic programming (GP)-based classifier. The proposed system consists of three steps. In the first step, the lung volume is segmented using thresholding and 3D-connected component labeling. In the second step, optimal multiple thresholding and rule-based pruning are applied to detect and segment nodule candidates. In this step, a set of features is extracted from the detected nodule candidates, and essential 3D and 2D features are subsequently selected. In the final step, a GP-based classifier (GPC) is trained and used to classify nodules and non-nodules. GP is suitable for detecting nodules because it is a flexible and powerful technique;as such, the GPC can optimally combine the selected features, mathematical functions, and random constants. Performance of the proposed system is then evaluated using the Lung Image Database Consortium (LIDC) database. As a result, it was found that the proposed method could significantly reduce the number of false positives in the nodule candidates, ultimately achieving a 94.1% sensitivity at 5.45 false positives per scan. (C) 2012 Elsevier Inc. All rights reserved.
Dynamic Workflow Scheduling in Fog Computing (DWSFC) is an important optimisation problem with many real-world applications. The current workflow scheduling problems only consider cloud servers but ignore the roles of...
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Dynamic Workflow Scheduling in Fog Computing (DWSFC) is an important optimisation problem with many real-world applications. The current workflow scheduling problems only consider cloud servers but ignore the roles of mobile devices and edge servers. Some applications need to consider the mobile devices, edge, and cloud servers simultaneously, making them work together to generate an effective schedule. In this article, a new problem model for DWSFC is considered and a new simulator is designed for the new DWSFC problem model. The designed simulator takes the mobile devices, edge, and cloud servers as a whole system, where they all can execute tasks. In the designed simulator, two kinds of decision points are considered, which are the routing decision points and the sequencing decision points. To solve this problem, a new Multi-Tree genetic programming (MTGP) method is developed to automatically evolve scheduling heuristics that can make effective real-time decisions on these decision points. The proposed MTGP method with a multi-tree representation can handle the routing decision points and sequencing decision points simultaneously. The experimental results show that the proposed MTGP can achieve significantly better test performance (reduce the makespan by up to 50%) on all the tested scenarios than existing state-of-the-art methods.
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