gene expression programming (GEP) is an evolutionary algorithm that combines the characteristics of genetic algorithm and\ngenetic programming by incorporating genotype and phenotype representations in its chromosome....
详细信息
gene expression programming (GEP) is an evolutionary algorithm that combines the characteristics of genetic algorithm and\ngenetic programming by incorporating genotype and phenotype representations in its chromosome. Although many methods exist for modeling and optimization, none can be considered universal, hence there is a never ending search for algorithms and\nmethods that can be applied in chemical process modeling and optimization.\n The main aim of this thesis is to explore the possibility of applying GEP to chemical process modeling and optimization. Having been invented in the recent past, there is a need to investigate its applicability to various fields. This thesis presents practical examples of steady-state modeling of styrene-butadiene rubber (SBR) formulation, concrete mix formulation and an ethylbenzene dehydrogenation reactor using GEP. It also presents results obtained from the global optimization of benchmark problems (both NLP and MINLP). In both cases the results obtained show that GEP can also be applied to chemical\nprocess modeling and optimization.
Fierce competition in today's economy forces companies to fully optimize their processes in order to supply customers with high-quality products on time with lowest possible cost. Designing optimal production line...
详细信息
Fierce competition in today's economy forces companies to fully optimize their processes in order to supply customers with high-quality products on time with lowest possible cost. Designing optimal production lines is a major step ahead in satisfying customer needs. Owing to the stochastic and highly nonlinear nature of the production lines, their optimal design is not easy and requires usage of advanced tools and techniques. In the present paper one of the new generation soft computing technique that is known as gene expression programming (GEP) is used to develop a meta-model from extensive simulation experiments for the multiple objective design of a production line. The developed meta-model is used to optimize production line design with multiple objective tabu search algorithm (MOTS). It is found out that GEP and MOTS can be effectively used to model and solve production line design problems.
gene expression programming (GEP) models, a robust variant of genetic programming, are developed in this study to correlate resilient modulus with routine properties of subgrade soils and state of stress for pavement ...
详细信息
gene expression programming (GEP) models, a robust variant of genetic programming, are developed in this study to correlate resilient modulus with routine properties of subgrade soils and state of stress for pavement design applications. A database used for building the model was developed that contained grain size distribution, Atterberg limits, standard Proctor, unconfined compression, and resilient modulus results for 97 soils from 16 different counties in Oklahoma. Of these, 63 soils (development data set) are used in training, and the remaining 34 soils (evaluation data set) from two different counties are used in evaluation of the models developed. Two different correlations were developed using different combinations of the influencing parameters. The proposed constitutive models relate the resilient modulus of routine subgrade soils to moisture content w, dry density d, plasticity index (PI), percent passing a No. 200 sieve (P200), unconfined compressive strength Uc, deviatoric stress sigma d, and bulk stress . The results are compared with those from artificial neutral network (ANN) models. Overall, GEP models show good performance and are proven to be better than ANN models. The GEP-based design equations can be readily used for pavement design purposes or may be used as a fast check on solutions developed by more in-depth deterministic analyses.
Longitudinal dispersion in pipelines leads to changes in the characteristics of contaminants. It is critical to quantify these changes because the contaminants travel through water networks or through chemical reactor...
详细信息
Longitudinal dispersion in pipelines leads to changes in the characteristics of contaminants. It is critical to quantify these changes because the contaminants travel through water networks or through chemical reactors. The essential characteristics of longitudinal dispersion in pipes can be described by the longitudinal dispersion coefficient. This paper presents the application of evolutionary gene expression programming (GEP) to develop new empirical formulas for the prediction of longitudinal dispersion coefficients in pipe flow using 220 experimental case studies of the dispersion coefficient with a R range of 2,000-500,000 spanning transitional and turbulent pipe flow. gene expression programming is used to develop empirical relations between the longitudinal dispersion coefficient and various control variables, including the Reynolds number, the average velocity, the pipe friction coefficient, and the pipe diameter. Four GEP models are developed, and the weight and importance of each control variable is presented. The prediction uncertainties of all of the developed GEP models are quantified and compared with those of existing models. Finally, a parametric analysis is performed for further verification of the developed GEP models. The results indicate that the proposed relations are simple and can effectively evaluate the longitudinal dispersion coefficients in pipe flow. (C) 2013 American Society of Civil Engineers.
gene expression programming (GEP) is a recently developed evolutionary computation method for model learning and knowledge discovery. Sometimes it is not easy when use GEP to solve too complex problem, so enhancing th...
详细信息
ISBN:
(纸本)9783540921363
gene expression programming (GEP) is a recently developed evolutionary computation method for model learning and knowledge discovery. Sometimes it is not easy when use GEP to solve too complex problem, so enhancing the algorithm learning capability is necessary. This paper proposes all immune principle based GEP algorithm (iGEP), which combines gene library and clonal election algorithm. The gene library is composed of subexpressions of GEP expression selected from the process of evolution. The proposed algorithm introduces some new features. including the best subexpression of GEP expression is selected as the solution of the problem, and some segments of gene library are used for hypermutation and receptor editing. In terms of convergence rate and computational efficiency, the experimented results on some benchmark problems of the UCI repository show that iGEP outperforms the standard GEP.
gene expression programming is new member of the genetic computing family which is developed from the geneexpression rules of biogenetics. This paper aim to solve the function modeling through applying the improved G...
详细信息
gene expression programming is new member of the genetic computing family which is developed from the geneexpression rules of biogenetics. This paper aim to solve the function modeling through applying the improved GEP algorithm and provide valuable forecasting function and instructional data for the practical work. The paper will introduce the application background of GEP, basic theories of GEP, at the same time, it proposes the corrective method of both evolutionary rate and numerical constants in order to realize the function modeling. Furthermore, experiment proves that GEP is more effective than the traditional methods.
gene expression programming is a new evolutionary algorithm that overcomes many limitations of the more established genetic Algorithms and genetic programming. Its application to event selection in high energy physics...
详细信息
gene expression programming is a new evolutionary algorithm that overcomes many limitations of the more established genetic Algorithms and genetic programming. Its application to event selection in high energy physics data analysis is presented using as an example application the selection of K-S particles produced in e(+)e(-) interactions at 10 GeV and reconstructed in the decay mode K-S -> pi(+)pi(-). The algorithm was used for automatic identification of classification criteria for signallbackground separation. For the problem studied and for data samples with signal to background ratios between 0.25 and 5, the classification accuracy obtained with the criteria developed by the GEP algorithm was in the range of 92-95%. (C) 2007 Elsevier B.V. All rights reserved.
A parallel hybrid framework that combines gene expression programming (GEP) as the evolutionary problem-solving methodology and alternative meta-heuristics for tuning parameter values of the parallel GEP runs is prese...
详细信息
A parallel hybrid framework that combines gene expression programming (GEP) as the evolutionary problem-solving methodology and alternative meta-heuristics for tuning parameter values of the parallel GEP runs is presented. The implementation of this framework is based on a client-server architecture which includes clients that use GEP to evolve candidate solutions for the problem in question, and clients that use (possibly) other meta-heuristics to tune GEP input parameters. In the implementation of this framework, a genetic algorithms methodology is used for parameter tuning. For testing the framework and its implementation, a suite of symbolic regression problems of different complexities is used. Our experimental results show that our approach provides a solution for the problem of automatically tuning two GEP input parameters, viz., the number of genes and the length of each gene. (C) 2007 Elsevier Inc. All rights reserved.
gene expression programming (GEP) is a novel machine learning technique. The GEP is used to build nonlinear quantitative structure activity relationship model for the prediction of the Percent of Applied Dose Dermally...
详细信息
gene expression programming (GEP) is a novel machine learning technique. The GEP is used to build nonlinear quantitative structure activity relationship model for the prediction of the Percent of Applied Dose Dermally Absorbed (PADA) over 24 h for polycyclic aromatic hydrocarbons. This model is based on descriptors which are calculated from the molecular structure. Three descriptors are selected from the descriptors pool by Heuristic Method (HM) to build a multivariable linear model. The GEP method produced a nonlinear quantitative model with a correlation coefficient and a mean error of 0.92 and 4.70 for the training set, 0.91 and 7.65 for the test set, respectively. It is shown that the GEP predicted results are in good agreement with experimental ones.
A hybrid evolutionary technique is proposed for data mining tasks, which combines a principle inspired by the immune system, namely the clonal selection principle, with a more common, though very efficient, evolutiona...
详细信息
A hybrid evolutionary technique is proposed for data mining tasks, which combines a principle inspired by the immune system, namely the clonal selection principle, with a more common, though very efficient, evolutionary technique, gene expression programming (GEP). The clonal selection principle regulates the immune response in order to successfully recognize and confront any foreign antigen, and at the same time allows the amelioration of the immune response across successive appearances of the same antigen. On the other hand, gene expression programming is the descendant of genetic algorithms and genetic programming and eliminates their main disadvantages, such as the genotype-phenotype coincidence, though it preserves their advantageous features. In order to perform the data mining task, the proposed algorithm introduces the notion of a data class antigen, which is used to represent a class of data. the produced rules are evolved by our clonal selection algorithm (CSA), which extends the recently proposed CLONALG algorithm. In CSA, among other new features, a receptor editing step has been incorporated. Moreover, the rules themselves are represented as antibodies that are coded as GEP chromosomes in order to exploit the flexibility and the expressiveness of such encoding. The proposed hybrid technique is tested on a set of benchmark problems in comparison to GEP. In almost all problems considered, the results are very satisfactory and outperform conventional GEP both in terms of prediction accuracy and computational efficiency.
暂无评论