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5th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)

作者： Yang, Lechan Li, Wenjuan Qin, Zhihao Deng, Song Tu, Lili Nanjing Univ Int Inst Earth Syst Sci Nanjing 210093 Jiangsu Peoples R China Chinese Acad Agr Sci Inst Agr Resources & Reg Planning Beijing 100081 Peoples R China Nanjing Univ Post & Telecommun Inst Adv Technol Nanjing 210003 Jiangsu Peoples R China

ISBN: (纸本)9781509023509

Estimating dynamically for leaf nitrogen concentration is an important subject in the studies on crop monitoring. The traditional regression methods including linear regression, partial least squares regression, support vector machine regression and random forest regression depended on a priori knowledge and many subjective factors. Moreover, these methods have high time complexity and low computational efficiency for complex and high-dimensional hyperspectral data. In order to better find reflectance model of LNC for complex and high-dimensional hyperspectral data, this paper presents distributed reflectance model mining of leaf nitrogen content by using gene expression programming (DRMMLNC-GEP) which combined with GEP and grid service. The comparative results show that the DRMMLNC-GEP outperforms all other algorithms on the average time-consumption, value of R-square and prediction accuracy. Meanwhile, experimental results also show that with the increasing of datasets size, DRMMLNC-GEP demonstrates good speed-up ratio and scale-up ratio too.

关键词： reflectance model leaf nitrogen content gene expression programming nonlinear least squares

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Person Identification from Visual Aesthetics Using gene expression programming 18

Person Identification from Visual Aesthetics Using Gene Expr...

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18th International Conference on Cyberworlds (CW)

作者： Sieu, Brandon Gavrilova, Marina Univ Calgary Dept Comp Sci Calgary AB Canada

ISBN: (纸本)9781728122977

The last decade has witnessed an increase in online human interactions, covering all aspects of personal and professional activities. Identification of people based on their behavior rather than physical traits is a growing industry, spanning diverse spheres such as online education, e-commerce and cyber security. One prominent behavior is the expression of opinions, commonly as a reaction to images posted online. Visual aesthetic is a soft, behavioral biometric that refers to a person's sense of fondness to a certain image. Identifying individuals using their visual aesthetics as discriminatory features is an emerging domain of research. This paper introduces a new method for aesthetic feature dimensionality reduction using gene expression programming. The advantage of this method is that the resulting system is capable of using a tree-based genetic approach for feature recombination. Reducing feature dimensionality improves classifier accuracy, reduces computation runtime, and minimizes required storage. The results obtained on a dataset of 200 Flickr users evaluating 40000 images demonstrates a 94% accuracy of identity recognition based solely on users' aesthetic preferences. This outperforms the best-known method by 13.5%.

关键词： Pattern recognition behavioral biometrics cybersecurity gene expression programming aesthetics

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Integration of Reaction Kinetics Theory and gene expression programming to Infer Reaction Mechanism

Integration of Reaction Kinetics Theory and Gene Expression ...

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作者： Jason R. White University of Connecticut

学位级别：博士

Mechanistic mathematical models of biological systems have been used to describe biological phenomena, including human disease, in the hope that one day these models may be used to better understand diseases, as well as to develop and optimize therapeutic strategies. Evolutionary algorithms, such as genetic programming, may be used to symbolically regress mathematical models describing chemical and biochemical species for which kinetic data are available. However, current evolutionary algorithms are restricted to the formulation of simple or approximate models due to the computational cost of evolving mechanistic models for more complex systems. It was hypothesized that chemical reaction kinetic theory could be used to sufficiently reduce the model search space for an evolutionary algorithm such that it would be possible to infer mechanistic mathematical models of complex biological interactions. An evolutionary algorithm capable of formulating mass action kinetic models of biological systems from time series data sets was developed for a system of n-species using heuristics from chemical reaction kinetic theory and a gene expression programming (GEP) based approach. The resulting algorithm was then successfully validated on a general model of viral dynamics that accounted for six pathways relating the change in viral template, viral genome, and viral structural protein concentrations over time. The algorithm was applied to generate cohort-specific models of HIV dynamics from a clinical data set. HIV-1 infection models were defined as sets of two ordinary differential equations describing the change in CD4+ T-cell and HIV-1 concentrations over time. The evolved models were used to generate hypotheses regarding treatment effectiveness and the potential for viral rebound in three cohorts of HIV-1 positive individuals receiving different Highly Active Antiretroviral Therapy (HAART) regimens. It was hypothesized by the algorithm that HAART was effective in stopping HIV-

关键词： network inference reaction kinetics genetic programming gene expression programming viral dynamics human immunodeficiency virus systems biology

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Population Diversity Strategy in gene expression programming

Population Diversity Strategy in Gene Expression Programming

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International Conference on Industry, Information System and Material Engineering

作者： Zhang, Yongqiang Xiao, Jing Hebei Univ Engn Informat & Elect Engn Inst Handan 056038 Peoples R China

ISBN: (纸本)9783037850275

Population diversity is one of the most important factors that influence the convergence speed and evolution efficiency of gene expression programming (GEP) algorithm. In this paper, the population diversity strategy of GEP (GEP-PDS) is presented, inheriting the advantage of superior population producing strategy and various population strategy, to increase population average fitness and decrease generations, to make the population maintain diversification throughout the evolutionary process and avoid "premature" to ensure the convergence ability and evolution efficiency. The simulation experiments show that GEP-PDS can increase the population average fitness by 10% in function finding, and decrease the generations for convergence to the optimal solution by 30% or more compared with other improved GEP.

关键词： gene expression programming GEP-PDS Local Optimum Function Finding

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An Improved gene expression programming for Fuzzy Classification

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3rd International Conference on Intelligence Computation and Applications

作者： Liu, Xiaobo Cai, Zhihua Gong, Wenyin China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China

ISBN: (纸本)9783540921363

Extracting accurate and understandable classification rules from data is a fundamental data mining activity. Fuzzy classification rules is considered a better classification of knowledge that the fuzzy rules of readability and analytical, and the use Of fuzzy rules is very intuitive. In this paper, we present an improved gene expression programming (GEP) for extracting fuzzy classification rules by a logical operators instead of mathematical ones to represent the chromosome validity evaluation. Moreover, a novel technique to evaluate the fitness of the individual rather than transform the chromosome into expression tree is proposed. Our proposed approach has been tested on some benchmark datasets selected from UCI and the results indicate that our approach is highly comparable with other techniques including basic GEP, C4.5 and C4.5 rule.

关键词： gene expression programming fuzzy classification rules logic operators expression tree

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genetic Neutrality in Naive gene expression programming

Genetic Neutrality in Naive Gene Expression Programming

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4th International Conference on Wireless Communications, Networking and Mobile Computing

作者： Zhu, Ming-fang Tang, Chang-jie Qiao, Shao-jie Dai, Shu-cheng Chen, Yu Sichuan Univ Sch Comp Chengdu 610064 Peoples R China

ISBN: (纸本)9781424421077

gene expression programming (CEP) is a genotype-phenotype system with non-coding regions in genome where possible neutral mutations would be accumulated. In this paper, a novel concept named naive gene expression programming (NGEP) is introduced. The main contributions include: (a) proposing a novel algorithm named NGEP, which decoding method based on the complete tree, (b) discussing the role of genetic neutrality in GEP and NGEP, and (c) performing experiments to show that NGEP is faster than traditional GEP, and the success rate of NGEP is higher than that in traditional GEP.

关键词： genetic neutrality gene expression programming naive gene expression programming evolutionary computation

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Evolution of Robotic Behaviours Using gene expression programming

Evolution of Robotic Behaviours Using Gene Expression Progra...

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2010 IEEE World Congress on Computational Intelligence

作者： Mwaura, Jonathan Keedwell, Ed Univ Exeter Coll Engn Math & Phys Sci Exeter EX4 4QF Devon England

ISBN: (纸本)9781424481262

genetic Algorithms and genetic programming have been used extensively in Evolutionary robotics (ER) with the goal of automatic programming of robotic controllers and has shown to be a promising approach. In this paper, we demonstrate the use of gene expression programming, GEP, a newly developed evolutionary algorithm akin to GA and GP, to evolve robotic behaviours. We use the already well known obstacle avoidance behaviour for our initial work. The behaviour can be regarded as emergent when the main aim is to develop a wandering/exploratory behaviour. From our investigations, we show that GEP is able to learn controllers for a number of different environments. Moreover, standard GEP has never been used before in evolving robotic behaviours, however due to its reported good performances in other fields, we feel it has the capability to be used in ER.

关键词： GA GEP GP automatic programming automatic programming collision avoidance evolutionary algorithm evolutionary robotics gene expression programming genetic algorithms genetic algorithms genetic programming genetics obstacle avoidance behaviour robotic behaviours robotic controllers

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Performance Emission Characterization of a LPG-Diesel Dual Fuel Operation: A gene expression programming Approach 4th

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4th International Conference on Harmony Search, Soft Computing and Applications (ICHSA)

作者： Chakraborty, Amitav Roy, Sumit Banerjee, Rahul NIT Agartala Dept Mech Engn Agartala 799046 India BML Munjal Univ Dept Mech Engn Gurgoan India

ISBN: (纸本)9789811307614;9789811307607

The envisaged work attempts to explore the inherent capability of LPG as a potent alternative fuel, in diesel dual fuel paradigms in order to address the omni-present BTE-NOx-SOOT trade-off perspectives of an existing diesel engine. Furthermore, considering the prohibitive costs of computational time of present day 3D CFD platforms in multi-objective calibration challenges in I.C. engine domains, a unique gene expression programming (GEP) model has been proposed, to act as a robust and computationally rational system identification tool (SIT) in the LPG-diesel dual fuel platform. For the developed model, load, LPG energy share and injection duration were the chosen input variables, whereas BSFCEQ, BTE, NOx, SOOT, and HC were the corresponding output responses. Subsequent to GEP modeling, it was revealed that developed GEP model was competent enough to map the experimental engine output parameters with higher and commendable ranges of accuracy. The obtained results of coefficient of correlation were in the ranges of 0.99262-0.99769, while the error metrics of mean absolute percentage error values were in the ranges of 1.03-3.08% and very low values root mean square errors, respectively.

关键词： gene expression programming Dual fuel LPG Diesel Performance emission

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Integration of Reaction Kinetics Theory and gene expression programming to Infer Reaction Mechanism 20th

Integration of Reaction Kinetics Theory and Gene Expression ...

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20th European Conference on the Applications of Evolutionary Computation (EvoApplications)

作者： White, Jason R. Srivastava, Ranjan Univ Connecticut Dept Chem & Biomol Engn Storrs CT 06269 USA Univ Calif Davis Dept Chem Engn One Shields Ave Davis CA 95616 USA

ISBN: (纸本)9783319558493;9783319558486

Mechanistic mathematical models of biomolecular systems have been used to describe biological phenomena in the hope that one day these models may be used to enhance our fundamental understanding of these phenomena, as well as to optimize and engineer biological systems. An evolutionary algorithm capable of formulating mass action kinetic models of biological systems from time series data sets was developed for a system of n-species. The strategy involved using a gene expression programming (GEP) based approach and heuristics based on chemical kinetic theory. The resulting algorithm was successfully validated by recapitulating a nonlinear model of viral dynamics using only a "noisy" set of time series data. While the system analyzed for this proof-of-principle study was relatively small, the approach presented here is easily parallelizable making it amenable for use with larger systems. Additionally, greater efficiencies may potentially be realized by further taking advantage of the problem domain along with future breakthroughs in computing power and algorithmic advances.

关键词： Evolutionary algorithm Biochemical kinetics Mechanistic modeling genetic programming gene expression programming

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Predicting Pavement Condition Index from International Roughness Index using gene expression programming

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INNOVATIVE INFRASTRUCTURE SOLUTIONS 2021年第3期6卷 1-12页

作者： Imam, Rana Murad, Yasmin Asi, Ibrahim Shatnawi, Anis Univ Jordan Civil Engn Dept Amman Jordan Arab Ctr Engn Studies Amman Jordan

Public agencies use pavement management systems to make objective decisions and maintain pavements above the minimum acceptable performance conditions at minimal costs. To achieve this goal, pavement condition is monitored, so its deterioration could be accurately predicted, in order to decide on any required maintenance and rehabilitation. The Pavement Condition Index (PCI) is a composite index used to assess the condition of flexible pavements. The International Roughness Index (IRI) is a smoothness or quality of ride indicator, which is the cumulative vertical movements or vibrations divided by the profile length. Collecting IRI is straightforward and much more affordable than collecting pavement distress data. Predicting the PCI for pavement management assessments without evaluating the extent and severity of the distresses saves costs and person-hours. In this paper, gene expression programming (GEP) was adopted for the first time to predict the PCI from the IRI, using data that was half compiled from the existing literature and the other half was measured and collected in the field by the authors. The PCI values predicted by the GEP model were compared to the estimated PCI values from applying models published in previous studies. The GEP model outperformed all the other available models in the literature, with a maximum R-2 of 82% for the complete dataset.

关键词： Pavement Condition Index International Roughness Index Macrotexture gene expression programming

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