Gene Expression Programming in Sensor Characterization: Numerical Results and Experimental Validation

Janeiro, Fernando M.; Santos, J.V. Araújo dos; Ramos, Pedro M.

doi:10.1109/tim.2012.2224275

Cited by 19 publications

(3 citation statements)

References 27 publications

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“…In fact, sensor characterization is used to get a model of it, typically learning from a data set. Examples of this method are reported in literature …”

Section: Resultsmentioning

confidence: 99%

The development of an artificial organic networks toolkit for LabVIEW

Ponce

Molina

2015

J. Comput. Chem.

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Two of the most challenging problems that scientists and researchers face when they want to experiment with new cutting-edge algorithms are the time-consuming for encoding and the difficulties for linking them with other technologies and devices. In that sense, this article introduces the artificial organic networks toolkit for LabVIEW™ (AON-TL) from the implementation point of view. The toolkit is based on the framework provided by the artificial organic networks technique, giving it the potential to add new algorithms in the future based on this technique. Moreover, the toolkit inherits both the rapid prototyping and the easy-to-use characteristics of the LabVIEW™ software (e.g., graphical programming, transparent usage of other softwares and devices, built-in programming event-driven for user interfaces), to make it simple for the end-user. In fact, the article describes the global architecture of the toolkit, with particular emphasis in the software implementation of the so-called artificial hydrocarbon networks algorithm. Lastly, the article includes two case studies for engineering purposes (i.e., sensor characterization) and chemistry applications (i.e., blood-brain barrier partitioning data model) to show the usage of the toolkit and the potential scalability of the artificial organic networks technique.

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“…In fact, sensor characterization is used to get a model of it, typically learning from a data set. Examples of this method are reported in literature …”

Section: Resultsmentioning

confidence: 99%

The development of an artificial organic networks toolkit for LabVIEW

Ponce

Molina

2015

J. Comput. Chem.

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“…The cost for search specific gene elements in a genetic operation is reduced from O(n) to O(1). GEP has superior performance in dealing with complex problems than conventional regression methods; for example, modeling sensor characteristics [27], diagnosis and prediction of lung cancer [28], fault diagnosis of power transformers [29], and intrusion detection of power grid [30]. Refer to [26] for more details about GEP.…”

Section: B Genetic Expression Programmingmentioning

confidence: 99%

Migration-Based Load Balance of Virtual Machine Servers in Cloud Computing by Load Prediction Using Genetic-Based Methods

Hung

Tsai

et al. 2021

IEEE Access

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This paper presents a two-stage genetic mechanism for the migration-based load balance of virtual machine hosts (VMHs) in cloud computing. Previous methods usually assume this issue as a jobassignment optimization problem and only consider the current VMHs' loads; however, without considering loads of VMHs after balancing, these methods can only gain limited effectiveness in real environments. In this study, two genetic-based methods are integrated and presented. First, performance models of virtual machines (VMs) are extracted from their creating parameters and corresponding performance measured in a cloud computing environment. The gene expression programming (GEP) is applied for generating symbolic regression models that describe the performance of VMs and are used for predicting loads of VMHs after load-balance. Secondly, with the VMH loads estimated by GEP, the genetic algorithm considers the current and the future loads of VMHs and decides an optimal VM-VMH assignment for migrating VMs and performing load-balance. The performance of the proposed method is evaluated in a real cloudcomputing environment, Jnet, wherein the aforementioned methods are implemented as a centralized load balancing mechanism. The experimental results show that our method outperforms previous methods, such as heuristics and statistics regression.

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“…In security assessment and other application areas, Khattab et al introduced gene expression programming into power system static security assessment [42]. To better design sensor equivalent circuit, Janeiro et al used GEP to determine a suitable equivalent circuit and choose a circuit component [43]. For combinatorial optimization problems, Sabar et al present a dynamic multiarmed bandit-gene expression programming hyper-heuristic [44].…”

Section: Function Finding In Wireless Sensor Networkmentioning

confidence: 99%

Distributed Global Function Model Finding for Wireless Sensor Network Data

Deng

Yang

et al. 2016

Applied Sciences

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Abstract:Function model finding has become an important tool for analysis of data collected from wireless sensor networks (WSNs). With the development of WSNs, a large number of sensors have been widely deployed so that the collected data show the characteristics of distribution and mass. For distributed and massive sensor data, traditional centralized function model finding algorithms would lead to a significant decrease in performance. To solve this problem, this paper proposes a distributed global function model finding algorithm for wireless sensor network data (DGFMF-WSND). In DGFMF-WSND, on the basis of gene expression programming (GEP), an adaptive population generation strategy based on sub-population associated evolution is applied to improve the convergence speed of GEP. Secondly, to solve the generation of global function model in distributed wireless sensor networks data, this paper provides a global model generation algorithm based on unconstrained nonlinear least squares. Four representative datasets are used to evaluate the performance of the proposed algorithm. The comparative results show that the improved GEP with adaptive population generation strategy outperforms all other algorithms on the average convergence speed, time-consumption, value of R-square, and prediction accuracy. Meanwhile, experimental results also show that DGFMF-WSND has a clear advantage in terms of time-consumption and error of fitting. Moreover, with increasing of dataset size, DGFMF-WSND also demonstrates good speed-up ratio and scale-up ratio.

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Gene Expression Programming in Sensor Characterization: Numerical Results and Experimental Validation

Cited by 19 publications

References 27 publications

The development of an artificial organic networks toolkit for LabVIEW

The development of an artificial organic networks toolkit for LabVIEW

Migration-Based Load Balance of Virtual Machine Servers in Cloud Computing by Load Prediction Using Genetic-Based Methods

Distributed Global Function Model Finding for Wireless Sensor Network Data

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