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International Conference on Autonomous Agents and Multiagent Systems (AAMAS)

作者： Zhong, Jinghui Luo, Linbo Cai, Wentong Lees, Michael Nanyang Technol Univ Sch Comp Engn Singapore Singapore Univ Amsterdam Sect Computat Sci Amsterdam Netherlands

ISBN: (纸本)9781450327381

Agent-based modelling of human crowds has now become an important and active research field, with a wide range of applications such as military training, evacuation analysis and digital game. One of the significant and challenging tasks in agent-based crowd modelling is the design of decision rules for agents, so as to reproduce desired emergent phenomena behaviors. The common approach in agent-based crowd modelling is to design decision rules empirically based on model developer's experiences and domain specific knowledge. In this paper, an evolutionary framework is proposed to automatically extract decision rules for agent-based crowd models, so as to reproduce an objective crowd behavior. To automate the rule extraction process, the problem of finding optimal decision rules from objective crowd behaviors is formulated as a symbolic regression problem. An evolutionary framework based on gene expression programming is developed to solve the problem. The proposed algorithm is tested using crowd evacuation simulations in three scenarios with differing complexity. Our results demonstrate the feasibility of the approach and shows that our algorithm is able to find decision rules for agents, which in turn can generate the prescribed macro-scale dynamics.

关键词： Agent-Based Modelling Crowd Simulation Decision Rules Evolutionary Algorithm gene expression programming

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Oil price forecasting using gene expression programming and artificial neural networks

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ECONOMIC MODELLING 2016年 54卷 40-53页

作者： Mostafa, Mohamed M. El-Masry, Ahmed A. Gulf Univ Sci & Technol Coll Business West Mishref Kuwait UK Mansoura Univ Plymouth Business Sch Mansoura Egypt Umm Al Qura Univ Mecca Saudi Arabia

This study aims to forecast oil prices using evolutionary techniques such as gene expression programming (GEP) and artificial neural network (NN) models to predict oil prices over the period from January 2, 1986 to June 12, 2012. Autoregressive integrated moving average (ARIMA) models are employed to benchmark evolutionary models. The results reveal that the GEP technique outperforms traditional statistical techniques in predicting oil prices. Further, the GEP model outperforms the NN and the ARIMA models in terms of the mean squared error, the root mean squared error and the mean absolute error. Finally, the GEP model also has the highest explanatory power as measured by the R-squared statistic. The results of this study have important implications for both theory and practice. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Oil price prediction gene expression programming Neural networks ARIMA

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Attribute reduction function mining algorithm based on gene expression programming

Attribute reduction function mining algorithm based on gene ...

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5th International Conference on Machine Learning and Cybernetics

作者： Yuan, Chang-An Tang, Chang-Jie Zuo, Jie Chen, An-Long Wen, Yuan-Guang Sichuan Univ Coll Comp Chengdu 610065 Peoples R China Guangxi Teacher Educ Univ Dept Informat Technol Nanning 530001 Peoples R China Guangxi Univ Coll Forestry Nanning 530001 Peoples R China

ISBN: (纸本)1424400600

When mining non-linearity function with large number of variables, traditional methods cannot effectively reduce the conditional attributes. To solve the problem, this paper proposes GEP-ARFM model. The model includes the concepts of Marginal gene, Marginal Fitness, and Revised Fitness and the algorithms of GEPAMF, GARFM-GEP, and SARFM-GEP. The comparison experiments show that (1) Both GARFM-GEP and SARFM-GEP can effectively reduce the conditional attributes to find the best function expression. (2) The precision of function expression by using SARFM-GEP is approximate with using GARFM-GEP algorithm. (3) SARFM-GEP method is 300 times faster than GARFTM-GEP in the case of 20 independent variables. (4) The fitness value of the function expression got by using GEP-ARFM model is 24.66% greater than the traditional method.

关键词： gene expression programming attribute reduction function mining marginal gene remnant distribution

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The Application of Macro-Economic Prediction Based on Improved gene expression programming

The Application of Macro-Economic Prediction Based on Improv...

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International Conference on Computational Intelligence and Natural Computing

作者： Yan Jingfeng Li Guoqing Xuchang Univ Coll Comp Sci & Technol Xuchang 461000 Henan Peoples R China

ISBN: (纸本)9780769536453

An Improved gene expression programming(IGEP) is proposed in this paper. It has some new features: 1) introducing a new individual coding;2) introducing a new way of creating constants;3) introducing a hybrid self-adaptive crossover-mutation operator, which can enhance the search ability and exploit the optimum offspring. To validate the performance of IGEP, this paper applies IGEP into the solution of the macro-economic predictions. The experimental results demonstrate that Improved GEP can automatically find better Optimization Model, based on which prediction will be generated much more exactly.

关键词： gene expression programming genetic algorithms macro-economy prediction

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Forecasting Euro - United States Dollar Exchange Rate with gene expression programming

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6th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Antoniou, Maria A. Georgopoulos, Efstratios F. Theofilatos, Konstantinos A. Likothanassis, Spiridon D. Univ Patras Pattern Recognit Lab Dept Comp Engn & Informat Patras 26500 Greece

ISBN: (纸本)9783642162381

In the current paper we present the application of our gene expression programming Environment in forecasting Euro-United States Dollar exchange rate. Specifically, using the GEP Environment we tried to forecast the value of the exchange rate using its previous values. The data for the EURO-USD exchange rate are online available from the European Central Bank (ECB). The environment was developed using the JAVA programming language, and is an implementation of a variation of gene expression programming. gene expression programming (GEP) is a new evolutionary algorithm that evolves computer programs (they can take many forms: mathematical expressions, neural networks, decision trees, polynomial constructs, logical expressions, and so on). The computer programs of GEP, irrespective of their complexity, are all encoded in linear chromosomes. Then the linear chromosomes are expressed or translated into expression trees (branched structures). Thus, in GEP, the genotype (the linear chromosomes) and the phenotype (the expression trees) are different entities (both structurally and functionally). This is the main difference between GEP and classical tree based genetic programming techniques.

关键词： gene expression programming genetic programming Evolutionary Algorithms System Modeling time series Euro-Dollar Exchange rate

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Code Reuse in gene expression programming

Code Reuse in Gene Expression Programming

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International Conference on Intelligent Structure and Vibration Control (ISVC 2011)

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

ISBN: (纸本)9783037850206

gene expression programming (GEP) is a kind of phenotype/genotype based evolutionary computation. Code reuse is an important issue in GEP. Various methods are used in current literature to achieve this task. In this paper, we compared six GEP based algorithms by experiments. We proved that although it's possible invent different kinds of code reuse strategies, current available strategies are powerful and efficient.

关键词： gene expression programming code reuse symbolic regression

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Application of gene expression programming (GEP) for the Prediction of Compressive Strength of Geopolymer Concrete

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MATERIALS 2021年第5期14卷 1106-1106页

作者： Khan, Mohsin Ali Zafar, Adeel Akbar, Arslan Javed, Muhammad Faisal Mosavi, Amir Natl Univ Sci & Technol NUST Mil Coll Engn MCE Dept Struct Engn Islamabad 44000 Pakistan City Univ Hong Kong Dept Architecture & Civil Engn Kowloon Hong Kong 999077 Peoples R China Comsats Univ Islamabad Dept Civil Engn Abbottabad 22060 Pakistan Tech Univ Dresden Fac Civil Engn D-01069 Dresden Germany Norwegian Univ Life Sci Sch Econ & Business N-1430 As Norway Obuda Univ John von Neumann Fac Informat H-1034 Budapest Hungary Oxford Brookes Univ John Sch Built Environm Oxford OX3 0BP England

For the production of geopolymer concrete (GPC), fly-ash (FA) like waste material has been effectively utilized by various researchers. In this paper, the soft computing techniques known as gene expression programming (GEP) are executed to deliver an empirical equation to estimate the compressive strength fc ' of GPC made by employing FA. To build a model, a consistent, extensive and reliable data base is compiled through a detailed review of the published research. The compiled data set is comprised of 298 fc ' experimental results. The utmost dominant parameters are counted as explanatory variables, in other words, the extra water added as percent FA (%EW), the percentage of plasticizer (%P), the initial curing temperature (T), the age of the specimen (A), the curing duration (t), the fine aggregate to total aggregate ratio (F/AG), the percentage of total aggregate by volume (%AG), the percent SiO2 solids to water ratio (% S/W) in sodium silicate (Na2SiO3) solution, the NaOH solution molarity (M), the activator or alkali to FA ratio (AL/FA), the sodium oxide (Na2O) to water ratio (N/W) for preparing Na2SiO3 solution, and the Na2SiO3 to NaOH ratio (Ns/No). A GEP empirical equation is proposed to estimate the fc ' of GPC made with FA. The accuracy, generalization, and prediction capability of the proposed model was evaluated by performing parametric analysis, applying statistical checks, and then compared with non-linear and linear regression equations.

关键词： artificial intelligence gene expression programming fly ash waste materials geopolymer regression analysis building materials sustainable construction materials smart cities sustainable concrete cement

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Investigating micellar-enhanced ultrafiltration (MEUF) of mercury and arsenic from aqueous solution using response surface methodology and gene expression programming

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SEPARATION AND PURIFICATION TECHNOLOGY 2022年 281卷 119880-119880页

作者： Yaqub, Muhammad Lee, Seung Hwan Lee, Wontae Kumoh Natl Inst Technol Dept Environm Engn 1 Yangho Dong Gumi 730701 Gyeongbuk South Korea

Heavy metals such as mercury and arsenic in water resources have become a global concern due to their toxic, persistent, bio-accumulative, and carcinogenic characteristics. To address health-related issues as well as environmental concerns, it is important to eliminate mercury and arsenic contaminants from water streams. In this study, mercury and arsenic ions were removed from aqueous solutions through micellar-enhanced ultrafiltration (MEUF) using sodium dodecyl sulfate (SDS) and cetylpyridinium chloride (CPC) as chelating agents, respectively. Response surface methodology (RSM), based on the full factorial design (FFD) method, was applied to optimize the process parameters. The relationship between the removal of targeted heavy metals and process variables, including pressure, metal concentration, pH, and molar ratio of surfactant to metal defined by the proposed quadratic models. The models were statistically significant, as shown by analysis of variance. Experimental results presented the optimum operating parameters for > 95% mercury removal were: 2.5 bar pressure, 10 ppm mercury concentration, SDS to mercury MR of 12:1, and pH 8.0. In the case of arsenic, >90% removal was achieved with pressure at 2.5 bar, 15 ppm arsenic concentration, and CPC to arsenic MR at 8:1, and pH 8.0. Statistical validation of RSM models using predicted and experimental results indicated that the proposed model effectively provided a relationship between targeted heavy metal removal and process variables. Additionally, the MEUF performance for mercury and arsenic removal from an aqueous solution was predicted using a gene expression programming model that presented a good fit with the experimental results.

关键词： Arsenic gene expression programming Mercury Micellar-enhanced ultrafiltration Response surface methodology

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generating decision rules for flexible capacity expansion problem through gene expression programming

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COMPUTERS & OPERATIONS RESEARCH 2020年 122卷 105003-105003页

作者： Hu, Junfei Guo, Peng Poh, Kim-Leng Northwestern Polytech Univ Sch Management Xian Peoples R China Natl Univ Singapore Dept Ind & Syst Engn Singapore Singapore

This paper proposes a novel approach for generating decision rules to exercise flexibility in capacity expansion. The proposed approach differs from other decision rule generation methods by integrating gene expression programming. This approach allows parameters to be automatically selected from a database and optimally combined to form decision rules, allowing both the structure and parameters of the decision rules to evolve. The generated decision rules support capacity expansion activities by clearly providing guidance to adjust the expansion level and timing according to the changing environment. The proposed approach was applied to a waste-to-energy system, to flexibly expand capacity under uncertainty. The empirical results demonstrate that the decision rules generated by our proposed approach improved system performance in terms of expected net present value, relative to decision rules generated by a method based on differential evolution algorithm. A sensitivity analysis was also conducted to investigate the effectiveness of the proposed approach under changes to the major assumptions, and results indicated that the generated decision rule can guide capacity expansion under different situations. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Flexibility Decision rules gene expression programming Capacity expansion

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Performance Prediction of Hybrid Bamboo-Reinforced Concrete Beams Using gene expression programming for Sustainable Construction

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MATERIALS 2023年第20期16卷 6788-6788页

作者： Waqas, Hafiz Ahmed Bahrami, Alireza Sahil, Mehran Poshad Khan, Adil Ejaz, Ali Shafique, Taimoor Tariq, Zain Ahmad, Sajeel Ozkilic, Yasin Onuralp Ghulam Ishaq Khan Inst Engn Sci & Technol Dept Civil Engn Topi 23640 Pakistan Univ Gavle Fac Engn & Sustainable Dev Dept Bldg Engn Energy Syst & Sustainabil Sci S-80176 Gavle Sweden Natl Univ Sci & Technol Natl Inst Transportat Struct Engn Dept Risalpur 23200 Pakistan Necmettin Erbakan Univ Dept Civil Engn TR-42090 Konya Turkiye Lebanese Amer Univ Dept Civil Engn Byblos 11022801 Lebanon

The building and construction industry's demand for steel reinforcement bars has increased with the rapid growth and development in the world. However, steel production contributes to harmful waste and emissions that cause environmental pollution and climate change-related problems. In light of sustainable construction practices, bamboo, a readily accessible and eco-friendly building material, is suggested as a viable replacement for steel rebars. Its cost-effectiveness, environmental sustainability, and considerable tensile strength make it a promising option. In this research, hybrid beams underwent analysis through the use of thoroughly validated finite element models (FEMs), wherein the replacement of steel rebars with bamboo was explored as an alternative reinforcement material. The standard-size beams were subjected to three-point loading using FEMs to study parameters such as the load-deflection response, energy absorption, maximum capacity, and failure patterns. Then, gene expression programming was integrated to aid in developing a more straightforward equation for predicting the flexural strength of bamboo-reinforced concrete beams. The results of this study support the conclusion that the replacement of a portion of flexural steel with bamboo in reinforced concrete beams does not have a detrimental impact on the overall load-bearing capacity and energy absorption of the structure. Furthermore, it may offer a cost-effective and feasible alternative.

关键词： green building material hybrid beams bamboo-reinforced concrete beam finite element model replacement gene expression programming

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