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Augmented gene expression programming: A population diversifying paradigm

Augmented Gene Expression Programming: A population diversif...

IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Kataria, Shreya Sangal, Somya Tyagi, Twishi Aggarwal, Swati Netaji Subhas Inst Technol Dept Comp Engn Delhi India

ISBN: (纸本)9781509060177

gene expression programming, a popular evolutionary paradigm, has acquired great attention from researchers in the domain of mathematical modeling. In view of its insufficiencies arising due to premature convergence, this paper presents an Augmented gene expression programming (AGEP) algorithm. Improvements suggested over classical GEP mechanism are (1) Opposition Based Learning to initialize the population of individuals to speed up convergence, (2) A diversifying clonal selection algorithm to eliminate bias towards fitter individuals, and (3) A population upliftment step to counter stagnancy over generations. A set of experiments related to function finding was conducted using AGEP and the results show a prominent improvement by AGEP over its classical counterpart, GEP and an improved version from authoritative literature (Niche technology of Outbreeding Fusion-OFN-GEP). The results have been used to reason that AGEP gives more accurate solutions at a better convergence rate.

关键词： Evolutionary algorithms gene expression programming premature convergence opposition based learning clonal selection

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Shear strength investigation of the carbon fiber reinforced polymer-wrapped concrete beams using gene expression programming and finite element analysis

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JOURNAL OF STRUCTURAL INTEGRITY AND MAINTENANCE 2022年第1期7卷 15-24页

作者： Al-Bodour, Wassel Murad, Yasmin Imam, Rana Smadi, Yahia Univ Jordan Civil Engn Dept Amman Jordan Jordan Univ Sci & Technol Mech Engn Dept Irbid Jordan

Existing analytical and numerical models available in the literature can predict the shear strength of CFRP in RC beams based on the FRP strains and this requires measuring the FRP strain values. This research aims to predict the shear strength of CFRP in RC beams without the need to calculate the strains in FRP. Therefore, gene expression programming (GEP) is used in this research to forecast the contribution of the CFRP to shear strength in the RC beams without the need for calculating the strain of the CFRP. A comparison was later performed amongst the empirical findings of the developed GEP model and other models available in published research. The adequate accuracy and highest predictive ability were noticed for the developed GEP model relative to the models published previously. Strengthened reinforced concrete (RC) beams with epoxy-bonded carbon fiber reinforced polymer (CFRP) sheets at various angles of orientation were examined to investigate their shear behavior. The experimental outcomes of the tested specimens were then applied to build a finite element (FE) model using ABAQUS. The proposed FE model was able to forecast the experimental behavior of the samples examined with sufficient precision.

关键词： Finite element CFRP sheets gene expression programming CFRP shear strength shear strength reinforced concrete beams angles

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On Using gene expression programming to Evolve Multiple Output Robot Controllers

On Using Gene Expression Programming to Evolve Multiple Outp...

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IEEE International Conference on Evolvable Systems (ICES)

作者： Mwaura, J. Keedwell, Ed Univ Pretoria Dept Comp Sci ZA-0002 Pretoria South Africa Univ Exeter CEMPS Dept Comp Sci Exeter Devon England

ISBN: (纸本)9781479944798

Most evolutionary algorithms (EAs) represents a potential solution to a problem as a single-gene chromosome encoding, where the chromosome gives only one output to the problem. However, where more than one output to a problem is required such as in classification and robotic problems, these EAs have to be either modified in order to deal with a multiple output problem or are rendered incapable of dealing with such problems. This paper investigates the parallelisation of genes as independent chromosome entities as described in the gene expression programming (GEP) algorithm. The aim is to investigate the capabilities of a multiple output GEP (moGEP) technique and compare its performance to that of a single-gene GEP chromosome (ugGEP). In the described work, the two GEP approaches are utilised to evolve controllers for a robotic obstacle avoidance and exploration behaviour. The obtained results shows that moGEP is a robust technique for the investigated problem class as well as for utilisation in evolutionary robotics.

关键词： gene expression programming Robot Control Evolutionary robotics

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International Journal Impact Factor Modeling based on gene expression programming

International Journal Impact Factor Modeling based on Gene E...

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6th International Conference on Wireless Communications, Networking and Mobile Computing (WICOM)

作者： Zhao, Yongxiang Zhou, Huaibei Li, Meifang Wuhan Univ Technol Sch Comp Sci & Technol Wuhan 430070 Peoples R China Wuhan Univ Technol State Key Lab Software Engn Wuhan 430070 Peoples R China Wuhan Univ Technol Sch Management Wuhan 430070 Peoples R China

ISBN: (纸本)9781424437092

Academic periodical is able to manifest scientific competitiveness of one country, and also is an important manifestation form of scientific productivity. Therefore, the research on international journal impact factor modeling has certain practical significance. Based on the gene expression programming, this paper establishes nonlinear model between journal impact factor and related indexes. The model utilizes immediacy index, total cites, articles, cited half-life, issues per year, self cites and average references as the inputs, and takes impact factor as the output. This paper mainly chooses the academic journals of China in 2008 as experimental data that are embodied by Journal Citation Reports. The experimental results show that impact factor is positively related to immediacy index, total cites and average references, and is negatively related to articles and cited half-life, and nearly has nothing to do with issues per year and self cites.

关键词： International journal impact factor gene expression programming JCR

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Virus Evolution Based gene expression programming for Classification Rules Mining

Virus Evolution Based Gene Expression Programming for Classi...

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International Conference on Materials, Mechatronics and Automation (ICMMA 2011)

作者： Wang Weihong Du Yanye Li Qu Zhejiang Univ Technol Coll Comp Sci Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9783037850176

gene expression programming(GEP) is a novel and accurate approach for classification. With the shortcoming of GEP, it often falls into the local optimums. In this paper, we introduce the virus evolutionary mechanism into GEP, with the infection operation of virus population, the diversity of the host population is increased, and the system is much easier to jump out of the local optimums, and much faster to obtain better results. Experiments on several benchmark data sets show that our approach can get close average accuracy and much better best accuracy compared with available results. What's more, the average execution time is largely decreased due to smaller population size and maximum generation.

关键词： gene expression programming virus evolution classification

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Evolving gene expression programming Classifiers for Ensemble Prediction of Movements on the Stock Market

Evolving Gene Expression Programming Classifiers for Ensembl...

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4th International Conference on Complex, Intelligent and Software Intensive Systems (CICIS)

作者： Bautu, Elena Bautu, Andrei Luchian, Henri Ovidius Univ Constanta Romania Mircea Cel Batran Naval Acad Constanta Romania Alexandru Ioan Cuza Univ Iasi Romania

ISBN: (纸本)9780769539676

Forecasting applications on the stock market attract much interest from researchers in the artificial intelligence field. The problem tackled in this study concerns predicting the direction of change of stock price indices, formulated in terms of binary classification. We use gene expression programming to evolve pools of binary classifiers and investigate several approaches to construct ensembles based on them. We compare the performance of the obtained classifiers with those of Naive Bayes, Support Vector Machines, Multilayer Perceptron, Decision Table and Random Forrest. The experiments performed on real-world stock market data show that the ensembles of GEP-evolved classifier models are competitive to classifiers trained by state-of-the-art machine learning methods.

关键词： forecasting direction of change gene expression programming ensemble classification binary time series

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RETRACTED: Prediction early age compressive strength of OPC-based geopolymers with different alkali activators and seashell powder by gene expression programming (Retracted article. See vol. 47, pg. 11828, 2021)

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CERAMICS INTERNATIONAL 2013年第2期39卷 1433-1442页

作者： Nazari, Ali Safarnejad, Mohammad Ghafouri Islamic Azad Univ Saveh Branch Dept Mat Engn Saveh Iran

In the present work, compressive strength of geopolymers produced by ordinary Portland cement, seashell powder and different alkali activators was predicted by gene expression programming design code. For constructing the models, 8 input parameters including the concentration of alkali solution, alkali binder to alkali solution weight ratio, alkali activator to cement+seashell powder weight ratio, oven curing temperature, water curing time, alkali solution type, alkali binder type and test trial number were considered. The output parameter was the compressive strength of the OPC+seashell powder based specimens. For collecting the data, 6 main factors each at 4 levels were considered for designing by the Taguchi method. A total of 32 series experiments (with 3 trials in each series) were conducted according to the L32 array proposed by the Taguchi method. According to the input parameters, the constructed models were trained and tested. From the total 96 obtained data, 70 sets were used for training and the remained 26 sets for testing the performance of the models. The results indicate that gene expression programming is a powerful tool for predicting the compressive strength of the geopolymers containing ordinary Portland cement, seashell powder and different alkali activators in the considered range with acceptable accuracy. (C) 2012 Elsevier Ltd and Techna Group S.r.l. All rights reserved.

关键词： Geopolymer Seashell powder Compressive strength gene expression programming

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Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A gene expression programming Approach

Optimizing Neural Architecture Search using Limited GPU Time...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Alves, Jeovane Honorio de Oliveira, Lucas Ferrari Fed Univ Parana UFPR Lab Vis Robot & Imaging VRI Curitiba Parana Brazil

ISBN: (纸本)9781728169293

Efficient identification of people and objects, segmentation of regions of interest and extraction of relevant data in images, texts, audios and videos are evolving considerably in these past years, which deep learning methods, combined with recent improvements in computational resources, contributed greatly for this achievement. Although its outstanding potential, development of efficient architectures and modules requires expert knowledge and amount of resource time available. In this paper, we propose an evolutionary-based neural architecture search approach for efficient discovery of convolutional models in a dynamic search space, within only 24 GPU hours. With its efficient search environment and phenotype representation, gene expression programming is adapted for network's cell generation. Despite having limited GPU resource time and broad search space, our proposal achieved similar state-of-the-art to manually-designed convolutional networks and also NAS-generated ones, even beating similar constrained evolutionary-based NAS works. The best cells in different runs achieved stable results, with a mean error of 2.82% in CIFAR-10 dataset (which the best model achieved an error of 2.67%) and 18.83% for CIFAR-100 (best model with 18.16%). For Imagenet in the mobile setting, our best model achieved top-1 and top-5 errors of 29.51% and 10.37%, respectively. Although evolutionary-based NAS works were reported to require a considerable amount of GPU time for architecture search, our approach obtained promising results in little time, encouraging further experiments in evolutionarybased NAS, for search and network representation improvements.

关键词： neural architecture search automl cnn deep learning gene expression programming

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Multicellular gene expression programming-Based Hybrid Model for Precipitation Prediction Coupled with EMD 14th

Multicellular Gene Expression Programming-Based Hybrid Model...

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14th International Conference on Intelligent Computing (ICIC)

作者： Li, Hongya Peng, Yuzhong Deng, Chuyan Pan, Yonghua Gong, Daoqing Zhang, Hao Guangxi Teachers Educ Univ Sch Comp & Informat Engn Key Lab Sci Comp & Intelligent Informat Proc Univ Nanning 530001 Peoples R China Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China

ISBN: (纸本)9783319959290;9783319959306

Accurate and timely precipitation prediction is very important to people's daily activities and production plans. However, the impact factors of meteorological precipitation are numerous and complex, making it difficult to predict, and the prediction effect by traditional methods is difficult to meet the public expectations. This work proposes to use Multicellular gene expression programming (MC_GEP) algorithm for modeling the historical precipitation data series decomposed by Empirical Mode Decomposition (EMD). Then we design a novel Multicellular gene expression programming based method coupled with Empirical Mode Decomposition, named as EMGEP2RP, for precipitation modeling and prediction. Using RMSE and MAE as evaluation indicators, simulation experiments were conducted on three different types of real precipitation data sets in different regions. The comparing results show that the EMGEP2RP algorithm significantly outperforms not only the existing gene expression programming (GEP) algorithm, but also the Back Propagation and Support Vector Machine algorithms which are widely used in meteorological modeling and predictions.

关键词： gene expression programming Empirical Mode Decomposition Precipitation modeling Precipitation prediction Time series prediction

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The Research on Evolution Schema Theorem on gene expression programming

The Research on Evolution Schema Theorem on Gene Expression ...

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International Conference on Emerging Computation and Information Technologies for Education (ECICE 2012)

作者： Cheng, Huifang Xue, Jingshun Hebei Univ Engn Sch Informat & Elect Engn Handan Peoples R China Xingtai Coll Elementary Educ Coll Xingtai Peoples R China

ISBN: (纸本)9783642284656

gene expression programming (GEP) from the proposed date, no complete theoretical system, a serious impediment to the development of GEP. To solve this problem, first studied in depth from a theoretical calculation model GEP: GEP gene model is defined and related concepts, the use of probabilistic methods detailed analysis of the single-gene GEP application examples in the evolution of the role of each operator, based on an analysis the results derived GEP schema theorem, proved by experiment GEP schema theorem is correct. GEP model theorem proposed improvements for the GEP algorithm provides quantitative assessment basis.

关键词： gene expression programming GEP model GEP schema theorem

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