Genetic Algorithm–Genetic Programming Approach to Identify Hierarchical Models for Ultraviolet Disinfection Reactors

Monroe, Jacob G.; Ducoste, Joel J.; Berglund, Emily Zechman

doi:10.1061/(asce)ee.1943-7870.0001492

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…To overcome this problem, a genetic algorithm is used to optimize the dataset that will be used in the training phase. The results of the previous research show that genetic algorithm can optimize the ability of classified distribution research by Monroe et al [33] developed a genetic programming approach to find the most suitable model structure and hierarchical parameter value. Modifications were made to the genetic programming approach to reduce model errors while limiting the growth of complex tree structures.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Architecture Model of Genetic Algorithm and Learning Vector Quantization Neural Network for Early Identification of Ear, Nose, and Throat Diseases

Hayat,

Soenandi

2024

J. Inf. Syst. Eng. Bus. Intell.

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Background: In 2020, the World Health Organization (WHO) estimated that 466 million people worldwide are affected by hearing loss, with 34 million of them being children. Indonesia is identified as one of the four Asian countries with a high prevalence of hearing loss, specifically at 4.6%. Previous research was conducted to identify diseases related to the Ear, Nose, and Throat, utilizing the certainty factor method with a test accuracy rate of 46.54%. The novelty of this research lies in the combination of two methods, the use of genetic algorithms for optimization and learning vector quantization to improve the level of accuracy for early identification of Ear, Nose, and Throat diseases. Objective: This research aims to produce a hybrid model between the genetic algorithm and the learning vector quantization neural network to be able to identify Ear, Nose, and Throat diseases with mild symptoms to improve accuracy. Methods: Implementing a 90:10 ratio means that 90% (186 data) of the data from the initial sequence is assigned for training purposes, while the remaining 10% (21 data) is allocated for testing. The procedural stages of genetic algorithm-learning vector quantization are population initialization, crossover, mutation, evaluation, selection elitism, and learning vector quantization training. Results The optimum hybrid genetic algorithm-learning vector quantization model for early identification of Ear, Nose, and Throat diseases was obtained with an accuracy of 82.12%. The parameter values with the population size 10, cr 0.9, mr 0.1, maximum epoch of 5000, error goal of 0.01, and learning rate (alpha) of 0.5. Better accuracy was obtained compared to backpropagation (64%), certainty factor 46.54%), and radial basic function (72%). Conclusion: Experiments in this research, successed identifying models by combining genetic algorithm-learning vector quantization to perform the early identification of Ear, Nose, and Throat diseases. For further research, it's very challenging to develop a model that automatically adapts the bandwidth parameters of the weighting functions during trainin Keywords: Early Identification, Ear-Nose-Throat Diseases, Genetic Algorithm, Learning Vector Quantization

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Section: Related Workmentioning

confidence: 99%

Hybrid Architecture Model of Genetic Algorithm and Learning Vector Quantization Neural Network for Early Identification of Ear, Nose, and Throat Diseases

Hayat,

Soenandi

2024

J. Inf. Syst. Eng. Bus. Intell.

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“…Genetic Algorithm (GA) is a superior machine learning technique inspired by Darwinism. Recent GA development has covered a wide range of applications, such as system identification [1], optimisation [2], [3], prognosis [4], data classification [5], feature selection [6], and image processing [7]. Nevertheless, the evolutionary searching nature of GA provides a double-edged sword: it is proven to be useful in executing heuristic optimisation but at the risk of premature convergence [8].…”

Section: Introductionmentioning

confidence: 99%

Self-Tune Linear Adaptive-Genetic Algorithm for Feature Selection

2019

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Genetic algorithm (GA) is an established machine learning technique used for heuristic optimisation purposes. However, this natural selection-based technique is prone to premature convergence, especially of the local optimum event. The presence of stagnant performance is due to low population diversity and fixed genetic operator setting. Therefore, an adaptive algorithm, the Self-Tune Linear Adaptive-GA (STLA-GA), is presented in order to avoid suboptimal solutions in feature selection case studies. STLA-GA performs parameter tuning for mutation probability rate, population size, maximum generation number and novel convergence threshold while simultaneously updating the stopping criteria by adopting an exploration-exploitation cycle. The exploration-exploitation cycle embedded in STLA-GA is a function of the latest classifier performance. Compared to standard feature selection practice, the proposed STLA-GA delivers multi-fold benefits, including overcoming local optimum solutions, yielding higher feature subset reduction rates, removing manual parameter tuning, eliminating premature convergence and preventing excessive computational cost, which is due to unstable parameter tuning feedback.

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Current developments in machine learning models with boosting algorithms for the prediction of water quality

Nguyen,

Wang,

Nguyen

et al. 2024

Water Security: Big Data-Driven Risk Identification, Assessment and Control of Emerging Contaminants

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Genetic Algorithm–Genetic Programming Approach to Identify Hierarchical Models for Ultraviolet Disinfection Reactors

Cited by 5 publications

References 35 publications

Hybrid Architecture Model of Genetic Algorithm and Learning Vector Quantization Neural Network for Early Identification of Ear, Nose, and Throat Diseases

Hybrid Architecture Model of Genetic Algorithm and Learning Vector Quantization Neural Network for Early Identification of Ear, Nose, and Throat Diseases

Self-Tune Linear Adaptive-Genetic Algorithm for Feature Selection

Current developments in machine learning models with boosting algorithms for the prediction of water quality

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