Genetic Algorithm Essentials

Krämer, Oliver

doi:10.1007/978-3-319-52156-5

Cited by 357 publications

(194 citation statements)

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“…A genetic algorithm is an optimization method based on Charles Darwin's evolution theory. The theory states that only the strongest individual will survive (Kramer 2017). It is considered as one of the best search tools for finding an optimal solution in a large searching space (Chakraborty 2010).…”

Section: Overview Of Encoding Technique In Genetic Algorithmsmentioning

confidence: 99%

“…When applying genetic algorithms in a trade study analysis, an encoding technique is necessary to allow the computers to understand the process (Chakraborty 2010). Encoding (Kramer 2017) is used to transform the phenotype (actual solutions space) to the genotype (the set of chromosomes) before the genetic algorithms can be applied to the trade study analysis (Cagan et al 2005).…”

Section: Overview Of Encoding Technique In Genetic Algorithmsmentioning

confidence: 99%

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Encoding Technique of Genetic Algorithms for Block Definition Diagram using OMG SysML^™ Notations

Arifin

Ong

Daengdej

et al. 2019

INCOSE International Symp

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Genetic algorithms can be used to perform an automated trade study analysis for component selection with the OMG Systems Modeling Language or OMG SysML™. However, the genetic algorithm encoding based on a single string with real‐value encoding failed to include the relationships in the OMG SysML™ model for the solution space. The genetic algorithm was then unable to perform an automated reasoning through the OMG SysML™ as a result. To solve this problem, this study proposes tree encoding as the encoding technique for the OMG SysML™ elements. Tree encoding offers a simple way to include blocks and their relationships into a genotype without altering its semantics. The scope of this study is limited to a preliminary investigation of the block definition diagram. The result from this study suggests that the conceptual idea can be applied to the genetic algorithm and the potential use of the approach in the trade study analysis, reasoning, and machine learning.

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Section: Overview Of Encoding Technique In Genetic Algorithmsmentioning

confidence: 99%

Section: Overview Of Encoding Technique In Genetic Algorithmsmentioning

confidence: 99%

Encoding Technique of Genetic Algorithms for Block Definition Diagram using OMG SysML^™ Notations

Arifin

Ong

Daengdej

et al. 2019

INCOSE International Symp

View full text Add to dashboard Cite

show abstract

“…To deal with such computationally expensive optimization problems, a viable option is to use a model that can approximate the outcomes from the fitness evaluations at relatively low computation cost and to limit the number of real evaluations (Tenne and Goh 2010). Kramer (2017) illustrated a workflow to embed a supervised learning model into a GA where the real fitness evaluations are performed only to the solutions that fulfil criterion in the predictive model. This way of integration is easily recognizable in different engineering applications (Marcelin 2004;Sreekanth and Datta 2011;Ibaraki, Tomita and Sugimoto 2015;Sato and Fujita 2016;Sakaguchi et al 2018).…”

Section: S U R V E Y -P a R A M E T E R U P D A T Ementioning

confidence: 99%

Acquisition design for direct reflectivity and velocity estimation from blended and irregularly sampled data

Nakayama

Blacquière

Ishiyama

2019

Geophysical Prospecting

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Blended acquisition along with efficient spatial sampling is capable of providing high‐quality seismic data in a cost‐effective and productive manner. While deblending and data reconstruction conventionally accompany this way of data acquisition, the recorded data can be processed directly to estimate subsurface properties. We establish a workflow to design survey parameters that account for the source blending as well as the spatial sampling of sources and detectors. The proposed method involves an iterative scheme to derive the survey design leading to optimum reflectivity and velocity estimation via joint migration inversion. In the workflow, we extend the standard implementation of joint migration inversion to cope with the data acquired in a blended fashion along with irregular detector and source geometries. This makes a direct estimation of reflectivity and velocity models feasible without the need of deblending or data reconstruction. During the iterations, the errors in reflectivity and velocity estimates are used to update the survey parameters by integrating a genetic algorithm and a convolutional neural network. Bio‐inspired operators enable the simultaneous update of the blending and sampling operators. To relate the choice of survey parameters to the performance of joint migration inversion, we utilize a convolutional neural network. The applied network architecture discards suboptimal solutions among newly generated ones. Conversely, it carries optimal ones to the subsequent step, which improves the efficiency of the proposed approach. The resultant acquisition scenario yields a notable enhancement in both reflectivity and velocity estimation attributable to the choice of survey parameters.

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“…Depending on the DSM objective, the Retailer solves for each hour h the optimization problems of Equations (5)- (7), Equations (8)- (16) or Equations (17)- (22). In the present paper, a binary coded Genetic Algorithm (GA) is utilized [74]. The outputs of the aforementioned optimization problems are prices p 1 (h), p 2 (h), p 1 (i), p 2 (i), p 1 (i'), p 2 (i'), p 1 (i") and p 2 (i").…”

Section: Price Elasticity Extractionmentioning

confidence: 99%

An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures

et al. 2017

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Abstract:Load profiling refers to a procedure that leads to the formulation of daily load curves and consumer classes regarding the similarity of the curve shapes. This procedure incorporates a set of unsupervised machine learning algorithms. While many crisp clustering algorithms have been proposed for grouping load curves into clusters, only one soft clustering algorithm is utilized for the aforementioned purpose, namely the Fuzzy C-Means (FCM) algorithm. Since the benefits of soft clustering are demonstrated in a variety of applications, the potential of introducing a novel modification of the FCM in the electricity consumer clustering process is examined. Additionally, this paper proposes a novel Demand Side Management (DSM) strategy for load management of consumers that are eligible for the implementation of Real-Time Pricing (RTP) schemes. The DSM strategy is formulated as a constrained optimization problem that can be easily solved and therefore, making it a useful tool for retailers' decision-making framework in competitive electricity markets.

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Genetic Algorithm Essentials

Cited by 357 publications

References 0 publications

Encoding Technique of Genetic Algorithms for Block Definition Diagram using OMG SysML^™ Notations

Encoding Technique of Genetic Algorithms for Block Definition Diagram using OMG SysML^™ Notations

Acquisition design for direct reflectivity and velocity estimation from blended and irregularly sampled data

An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures

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Genetic Algorithm Essentials

Cited by 357 publications

References 0 publications

Encoding Technique of Genetic Algorithms for Block Definition Diagram using OMG SysML™ Notations

Encoding Technique of Genetic Algorithms for Block Definition Diagram using OMG SysML™ Notations

Acquisition design for direct reflectivity and velocity estimation from blended and irregularly sampled data

An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures

Contact Info

Product

Resources

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Encoding Technique of Genetic Algorithms for Block Definition Diagram using OMG SysML^™ Notations

Encoding Technique of Genetic Algorithms for Block Definition Diagram using OMG SysML^™ Notations