Using genetic algorithms to estimate confidence intervals for missing spatial data

Eklund, Neil

doi:10.1109/tsmcc.2006.875407

Cited by 13 publications

(5 citation statements)

References 22 publications

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“…The genetic algorithm is a random search algorithm based on natural selection and biological genetic mechanisms (Eklund, 2006). Compared with other heuristic algorithms, it has good global search capability and efficiency, preventing the result from falling into the optimal local solution (Ye et al ., 2018).…”

Section: Methodsmentioning

confidence: 99%

Research on quality control methods for surface temperature observations via spatial correlation analysis

Xiong

Jiang

Tang

et al. 2022

Intl Journal of Climatology

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This article proposed a new quality control method (CS-MSF) to identify potential outliers in the surface temperature observations. The CS-MSF method employed cosine similarity and moving surface fitting to obtain the estimated value of the target station. For the regions with complex terrain and low weather station density, another quality control method (CE-GBDT) was employed to compensate for the shortcomings of CS-MSF. Compared to the spatial regression test method (SRT) and inverse distance weighting method (IDW), the results indicated that CS-MSF outperformed SRT and IDW in all the cases. And CE-GBDT was superior to the other methods for the regions with complex terrain and low weather station density. The comparison results led to the recommendation that the two proposed methods are effective quality control methods in identifying the seeded errors for the surface temperature observations.

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Section: Methodsmentioning

confidence: 99%

Research on quality control methods for surface temperature observations via spatial correlation analysis

Xiong

Jiang

Tang

et al. 2022

Intl Journal of Climatology

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“…The encoding strategy is different for different optimization problems, and a given problem may have more than one workable encoding strategy (Snyder and Daskin, 2006 (Michalewicz, 1996;Eklund, 2006). The coding procedure for the educational timetabling problems solved by GA in the literature is mostly based on matrix and vector.…”

Section: Random Key Based Genetic Algorithm (Rkga) Approach For Coursmentioning

confidence: 99%

“…A good encoding method can make finding good solutions relatively efficient. Conversely, a poor chromosome encoding method can make finding good solutions nearly impossible (Michalewicz, 1996;Eklund, 2006). The coding procedure for the educational timetabling problems solved by GA in the literature is mostly based on matrix and vector.…”

Section: Random Key Based Genetic Algorithm (Rkga) Approach For Course-room-time Assignmentmentioning

confidence: 99%

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A New Mathematical Model and Random Key Based Metaheuristic Solution Approach for Course-Room-Time Assignment Problem

Öztürk

Sagir

2019

Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi

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Course-room-time assignment problem, Mathematical modelling, Random key based genetic algorithms.This study presents a newly developed mixed-integer mathematical model for university course-room-time assignment problem. Optimal results with no soft constraint violations are obtained for some type of problem instances. As problem complexity increases it becomes more difficult to find feasible solution for this problem in a reasonable time. Therefore, a heuristic approach is often needed for such problems. In this study, a random key based genetic algorithm (RKGA) is developed. RKGA encoding is used in order to encode the chromosomes with a length of just the number of courses and not to use problem specific genetic operators and/or repair mechanisms. Well-known problem instances from the literature are selected to evaluate the outcome. The performance of RKGA is competitive to that of other algorithms especially for big size problems. Öz Ders-derslik-zaman dilimi atama problemi, Matematiksel modelleme, Rassal anahtar temelli genetik algoritma.Bu çalışmada üniversite ders-derslik-zaman dilimi atama problemi için yeni bir karma tam sayılı matematiksel model önerilmiştir. Geliştirilen karma tam sayılı matematiksel model ile literatürde yer alan test problemleri çözdürülmüş ve bir kısmı için tüm esnek kısıtlar sağlanarak en iyi çözüm elde edilmiştir. Problem karmaşıklığı arttıkça makul sürelerde uygun çözüm bulmak zorlaştığından, bu tür problemlerin çözümü için sezgisel bir yaklaşıma ihtiyaç duyulmaktadır. Çalışmada, rassal anahtar temelli bir genetik algoritma (RKGA) geliştirilmiştir. Probleme özgü özel genetik operatörler ve/veya onarma mekanizmaları kullanmamak için sadece ders sayısı uzunluğundaki kromozomları kodlamak için RKGA kodlaması kullanılmıştır. Çıktıların değerlendirilmesi için literatürde iyi bilinen test problemleri seçilmiştir. Özellikle büyük boyutlu problemlerde RKGA'nın performansının diğer algoritmalar ile rekabet edebilir düzeyde olduğu görülmüştür. Araştırma Makalesi defined as assigning courses to rooms and timeslots in such a way that there are no conflicts or clashes by satisfying objectives and the examination timetabling basically involves allocating a set of examinations to a set of rooms and time periods. The basic difference is that in course timetabling there cannot be more than one course per room in contrary of exam one. Since 1960's, when the timetabling problems have Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Sets and indices J = {j | j=1,…, n} for courses I = {i | i =1,…, m} for students K = {k | k = 1,…, h} for rooms L = {l | l = 1,… , o} for features T = {t | t =1,…, 45 } for timeslots D = {d | d =1,…, 5 } for days Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık

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“…Similar to the interpolation-based methods, the similarity-based methods also ignore the correlation among multiple attributes, which limit the accuracy of these algorithms [12]. The fourth category mainly includes point estimation and interval estimation [13], [14]. This kind of method uses the existing data to fill in missing attributes through the maximum likelihood estimation.…”

mentioning

confidence: 99%

Data-driven Missing Data Imputation for Wind Farms Using Context Encoder

Liao

Bak‐Jensen

Pillai

et al. 2022

Journal of Modern Power Systems and Clean Energy

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High-quality datasets are of paramount importance for the operation and planning of wind farms. However, the datasets collected by the supervisory control and data acquisition (SCADA) system may contain missing data due to various factors such as sensor failure and communication congestion. In this paper, a data-driven approach is proposed to fill the missing data of wind farms based on a context encoder (CE), which consists of an encoder, a decoder, and a discriminator. Through deep convolutional neural networks, the proposed method is able to automatically explore the complex nonlinear characteristics of the datasets that are difficult to be modeled explicitly. The proposed method can not only fully use the surrounding context information by the reconstructed loss, but also make filling data look real by the adversarial loss. In addition, the correlation among multiple missing attributes is taken into account by adjusting the format of input data. The simulation results show that CE performs better than traditional methods for the attributes of wind farms with hallmark characteristics such as large peaks, large valleys, and fast ramps. Moreover, the CE shows stronger generalization ability than traditional methods such as auto-encoder, K-means, k-nearest neighbor, back propagation neural network, cubic interpolation, and conditional generative adversarial network for different missing data scales.

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Using genetic algorithms to estimate confidence intervals for missing spatial data

Cited by 13 publications

References 22 publications

Research on quality control methods for surface temperature observations via spatial correlation analysis

Research on quality control methods for surface temperature observations via spatial correlation analysis

A New Mathematical Model and Random Key Based Metaheuristic Solution Approach for Course-Room-Time Assignment Problem

Data-driven Missing Data Imputation for Wind Farms Using Context Encoder

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