Modeling of road-traffic noise with the use of genetic algorithm

Rahmani, Simindokht; Mousavi, Seyed Mahmoud; Kamali, Mohamad Javad

doi:10.1016/j.asoc.2010.01.022

Cited by 104 publications

(49 citation statements)

References 28 publications

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“…within a building, using genetic algorithms. GA are also used in issues related to domains such as mathematics, statistics, physics, engineering, transportation, pollution cases [16], chemistry [17], agriculture [18], web programming [19], web applications [20] and even fashion issues [21]. This is a very short list of the applications of genetic algorithms in specific domains.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

A Method Based on Genetic Algorithms for Generating Assessment Tests Used for Learning

Anastasiu¹,

Bold²,

Nijloveanu³

2016

Polibits

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Abstract-Tests are used in a variety of contexts in the activity of everyday and everywhere learning. They are a specific method in the process of assessment (evaluation), which is an important part of the educational activity. Setting an optimized sequence of tests (SOT) originating from a group of tests which have the same subject, with certain restrictions corresponding to a certain wish of the evaluator can be a slowly time-consuming task, because the restriction can be various and the number of tests can be high. In this matter, this paper presents a method of generating optimized sequences of tests within a battery of tests using a genetic algorithm. We associate a number of representative keywords with a test. The user expresses the restriction by setting up a number of keywords which approximate best the subject wanted to be tested. The genetic algorithm helps in finding the optimized solutions and uses a less amount of hardware resources.

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Section: Genetic Algorithmsmentioning

confidence: 99%

A Method Based on Genetic Algorithms for Generating Assessment Tests Used for Learning

Anastasiu¹,

Bold²,

Nijloveanu³

2016

Polibits

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“…Traffic light control (TLC) and optimization have received special attention in recent years. From the previous studies, most of RTC is closely related to weak traffic control system or more precisely an inefficient TLC system [1], [11]- [14]. Therefore, research in reducing RTC by manipulating TLC system is relevant to be studied where the focus of this study is at Changlun main road traffic system, Kedah.…”

Section: Introductionmentioning

confidence: 99%

Green supply chain: Simulating road traffic congestion

Jalal

Nawawi

Desa

et al. 2017

J. Phys.: Conf. Ser.

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“…Recently, several prediction models were developed using artificial neural networks (ANNs) (Genaro et al, 2010;Givargis and Karimi, 2010) and genetic algorithms (Gundogdu et al, 2005;Rahmani et al, 2011). This paper presents a research of application of soft computing techniques for improvement of conventional methodologies for traffic noise prediction.…”

Section: Introductionmentioning

confidence: 99%

Assessment of traffic noise levels in urban areas using different soft computing techniques

Tomić¹,

Bogojević²,

Pljakić³

et al. 2016

The Journal of the Acoustical Society of America

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Available traffic noise prediction models are usually based on regression analysis of experimental data, and this paper presents the application of soft computing techniques in traffic noise prediction. Two mathematical models are proposed and their predictions are compared to data collected by traffic noise monitoring in urban areas, as well as to predictions of commonly used traffic noise models. The results show that application of evolutionary algorithms and neural networks may improve process of development, as well as accuracy of traffic noise prediction.

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Modeling of road-traffic noise with the use of genetic algorithm

Cited by 104 publications

References 28 publications

A Method Based on Genetic Algorithms for Generating Assessment Tests Used for Learning

A Method Based on Genetic Algorithms for Generating Assessment Tests Used for Learning

Green supply chain: Simulating road traffic congestion

Assessment of traffic noise levels in urban areas using different soft computing techniques

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