This paper models acidolysis of triolein and palmitic acid under the catalysis of immobilized sn-1,3 specific lipase. A gene-expression programming (GEP), which is an extension to genetic programming (GP)-based model ...
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This paper models acidolysis of triolein and palmitic acid under the catalysis of immobilized sn-1,3 specific lipase. A gene-expression programming (GEP), which is an extension to genetic programming (GP)-based model was developed for the prediction of the concentration of major reaction products of this reaction (1-palmitoyl-2,3-oleoyl-glycerol (POO), 1,31-dipalmitoyl-2-oleoyl-glycerol (POP) and triolein (OOO). Substrate ratio (SR), reaction temperature (T) and reaction time (t) were used as input parameters. The predicted models were able to predict the progress of the reactions with a mean standard error (MSE) of less than 1.0 and R of 0.978. Explicit formulation of proposed GEP models was also presented. Considerable good performance was achieved in modelling acidolysis reaction by using GEP. The predictions of proposed GEP models were compared to those of neural network (NN) modelling, and strictly good agreement was observed between the two predictions. Statistics and scatter plots indicate that the new GEP formulations can be an alternative to experimental models. (C) 2009 Elsevier Ltd. All rights reserved.
Storm surge is a genuine common fiasco coming from the ocean. Therefore, an exact forecast of surges is a vital assignment to dodge property misfortunes and to decrease a chance caused by tropical storm surge. genetic...
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Storm surge is a genuine common fiasco coming from the ocean. Therefore, an exact forecast of surges is a vital assignment to dodge property misfortunes and to decrease a chance caused by tropical storm surge. genetic programming (GP) is an evolution-based model learning technique that can simultaneously find the functional form and the numeric coefficients for the model. Therefore, GP has been widely applied to build models for predictive problems. However, GP has seldom been applied to the problem of storm surge forecasting. In this paper, we propose a new method to use GP for evolving models for storm surge forecasting. Experimental results on datasets collected from the Tottori coast of Japan show that GP can evolve accurate storm surge forecasting models. Moreover, GP can automatically select relevant features when evolving storm surge forecasting models, and the models evolved by GP are interpretable.
This study is a pioneer work that proposes genetic programming (GP) as a new approach for the explicit formulation of available rotation capacity of wide-flange beams which is an important phenomenon that determines t...
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This study is a pioneer work that proposes genetic programming (GP) as a new approach for the explicit formulation of available rotation capacity of wide-flange beams which is an important phenomenon that determines the plastic behaviour of steel structures. The database for the GP formulation is based on extensive experimental results from literature. The results of the GP-based formulation are compared with numerical results obtained by a specialized computer program and existing analytical equations. The results indicate that the proposed GP formulation performs quite well compared to numerical results and existing analytical equations and is quite practical for use. (c) 2006 Elsevier Ltd. All rights reserved.
In this paper, a genetic programming method for satellite system design is proposed to simultaneously optimize the topology and parameters of a satellite system. Firstly, the representation of satellite system design ...
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In this paper, a genetic programming method for satellite system design is proposed to simultaneously optimize the topology and parameters of a satellite system. Firstly, the representation of satellite system design is defined according to the tree structure. The genetic programming method is designed based on that representation. Secondly, according to the tree structure of different satellite schemes, different multiscale satellite models are established, in which various physical fields couple together. Then, an evaluation system is also proposed to test the performances of different satellite schemes. Finally, the application to the design of an earth observation satellite demonstrates the effectiveness of the proposed method.
Search Based Software Engineering techniques are emerging as important tools for software maintenance. Foremost among these is genetic Improvement, which has historically applied the stochastic techniques of genetic P...
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Search Based Software Engineering techniques are emerging as important tools for software maintenance. Foremost among these is genetic Improvement, which has historically applied the stochastic techniques of genetic programming to optimize pre-existing program code. Previous work in this area has not generally preserved program semantics and this article describes an alternative to the traditional mutation operators used, employing deterministic proof search in the sequent calculus to yield semantics-preserving transformations on algebraic data types. Two case studies are described, both of which are applicable to the recently-introduced 'grow and graft' technique of genetic Improvement: the first extends the expressiveness of the 'grafting' phase and the second transforms the representation of a list data type to yield an asymptotic efficiency improvement.
Social media sites, which became central to our everyday lives, enable users to freely express their opinions, feelings, and ideas due to a certain level of depersonalization and anonymity they provide. If there is no...
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Social media sites, which became central to our everyday lives, enable users to freely express their opinions, feelings, and ideas due to a certain level of depersonalization and anonymity they provide. If there is no control, these platforms may be used to propagate hate speech. In fact, in recent years, hate speech has increased on social media. Therefore, there is a need to monitor and prevent hate speech on these platforms. However, manual control is not feasible due to the high traffic of content production on social media sites. Moreover, the language used and the length of the messages provide a challenge when using classical machine learning approaches as prediction methods. This paper presents a genetic programming (GP) model for detecting hate speech where each chromosome represents a classifier employing a universal sentence encoder as a feature. A novel mutation technique that affects only the feature values in combination with the standard one-point mutation technique improved the performance of the GP model by enriching the offspring pool with alternative solutions. The proposed GP model outperformed all state-of-the-art systems for the four publicly available hate speech datasets.
This study is motivated by the empirical findings that news and social media Twitter messages (tweets) exhibit persistent predictive power on financial market movement. Based on the evidence that tweets are faster tha...
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This study is motivated by the empirical findings that news and social media Twitter messages (tweets) exhibit persistent predictive power on financial market movement. Based on the evidence that tweets are faster than news in revealing new market information, whereas news is regarded broadly a more reliable source of information than tweets, we propose a superior trading strategy based on the sentiment feedback strength between the news and tweets using generic programming optimization method. The key intuition behind this feedback strength based approach is that the joint momentum of the two sentiment series leads to significant market signals, which can be exploited to generate superior trading profits. With the trade-off between information speed and its reliability, this study aims to develop an optimal trading strategy using investors' sentiment feedback strength with the objective to maximize risk adjusted return measured by the Sterling ratio. We find that the sentiment feedback based strategies yield superior market returns with low maximum drawdown over the period from 2012 to 2015. In comparison, the strategies based on the sentiment feedback indicator generate over 14.7% Sterling ratio compared with 10.4% and 13.6% from the technical indicator-based strategies and the basic buy-and-hold strategy respectively. After considering transaction costs, the sentiment indicator based strategy outperforms the technical indicator based strategy consistently. Backtesting shows that the advantage is statistically significant The result suggests that the sentiment feedback indicator provides support in controlling loss with lower maximum drawdown. (C) 2017 Elsevier B.V. All rights reserved.
genetic programming (GP) is an automated method for creating computer programs starting from a high-level description of the problem to be solved. Many variants of GP have been proposed in the recent years. In this pa...
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genetic programming (GP) is an automated method for creating computer programs starting from a high-level description of the problem to be solved. Many variants of GP have been proposed in the recent years. In this paper we are reviewing the main GP variants with linear representation. Namely, Linear genetic programming, Gene Expression programming, Multi Expression programming, Grammatical Evolution, Cartesian genetic programming and Stack-Based genetic programming. A complete description is provided for each method. The set of applications where the methods have been applied and several Internet sites with more information about them are also given.
Manifold learning (MaL) methods are an invaluable tool in today's world of increasingly huge datasets. MaL algorithms can discover a much lower-dimensional representation (embedding) of a high-dimensional dataset ...
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Manifold learning (MaL) methods are an invaluable tool in today's world of increasingly huge datasets. MaL algorithms can discover a much lower-dimensional representation (embedding) of a high-dimensional dataset through nonlinear transformations that preserve the most important structure of the original data. State-of-the-art MaL methods directly optimize an embedding without mapping between the original space and the discovered embedded space. This makes interpretability-a key requirement in exploratory data analysis-nearly impossible. Recently, genetic programming has emerged as a very promising approach to MaL by evolving functional mappings from the original space to an embedding. However, genetic programming-based MaL has struggled to match the performance of other approaches. In this work, we propose a new approach to using genetic programming for MaL, which preserves local topology. This is expected to significantly improve performance on tasks where local neighborhood structure (topology) is paramount. We compare our proposed approach with various baseline MaL methods and find that it often outperforms other methods, including a clear improvement over previous genetic programming approaches. These results are particularly promising, given the potential interpretability and reusability of the evolved mappings.
This is a pioneer study that presents genetic programming (GP) as a new tool for prediction of local scour downstream of grade-control structures. The objective of this study is to provide an alternative formulation t...
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This is a pioneer study that presents genetic programming (GP) as a new tool for prediction of local scour downstream of grade-control structures. The objective of this study is to provide an alternative formulation to conventional regression based equations and verify the superiority of GP over regression analysis. The training and testing patterns of the proposed GP formulation are based on well established and widely dispersed experimental results from the literature. Linear and nonlinear regression-based equations were derived throughout regression analysis on dimensionless parameters obtained from dimensional analysis. The GP-based formulation results are compared with experimental results and other equations and found to be more accurate.
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