A conjugated evolutionary algorithm for hyperparameter optimization

Japa, Luis; Serqueira, Marcello; Mendonça, Israel; Bezerra, Eduardo; Aritsugi, Masayoshi; González, Pedro Henrique

doi:10.1109/cec55065.2022.9870442

Cited by 7 publications

(3 citation statements)

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“…The search for optimal hyperparameters for the different data sets and classes led to different results. The difference can be attributed to the different complexity of the data sets 16 . By using different scene parameters, the synthetic data set has a higher diversity of images, whereas the real data set has a higher homogeneity due to similar environmental parameters.…”

Section: Discussionmentioning

confidence: 99%

Medical instrument detection with synthetically generated data

Wiese,

Hinz,

Reithmeier

2024

Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications

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The persistent need for more qualified personnel in operating theatres exacerbates the remaining staff's workload. This increased burden can result in substantial complications during surgical procedures. To address this issue, this research project works on a comprehensive operating theatre system. The system offers real-time monitoring of all surgical instruments in the operating theatre, aiming to alleviate the problem. The foundation of this endeavour involves a neural network trained to classify and identify eight distinct instruments belonging to four distinct surgical instrument groups. A novel aspect of this study lies in the approach taken to select and generate the training and validation data sets. The data sets used in this study consist of synthetically generated image data rather than real image data. Additionally, three virtual scenes were designed to serve as the background for a generation algorithm. This algorithm randomly positions the instruments within these scenes, producing annotated rendered RGB images of the generated scenes. To assess the efficacy of this approach, a separate real data set was also created for testing the neural network. Surprisingly, it was discovered that neural networks trained solely on synthetic data performed well when applied to real data. This research paper shows that it is possible to train neural networks with purely synthetically generated data and use them to recognise surgical instruments in real images.

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Section: Discussionmentioning

confidence: 99%

Medical instrument detection with synthetically generated data

Wiese,

Hinz,

Reithmeier

2024

Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications

View full text Add to dashboard Cite

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“…Similarly, we show the results of cases r = 0.2, r = 0.6 and r = 1.0. Here, the r = 1.0 case is equivalent to our original version developed in [12], with the addition of the diversity control component. These experiments were also repeated a total of 10 times for each dataset.…”

Section: ) Random Walk and Bayesian Walkmentioning

confidence: 99%

“…In particular, we use a population-based optimization algorithm, Biased Random Key Genetic Algorithm (BRKGA) to create a hybrid algorithm called HyperBRKGA by applying an exploitation step to each candidate solution. In [12] we developed a conjugated evolutionary algorithm for hyperparameter optimization that showed promising results when compared to other commonly used methods. HyperBRKGA builds upon this version with the addition of the components later described in Section III.…”

Section: Introductionmentioning

confidence: 99%

A Population-Based Hybrid Approach for Hyperparameter Optimization of Neural Networks

et al. 2023

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Hyperparameter optimization is a fundamental part of Auto Machine Learning (AutoML) and it has been widely researched in recent years; however, it still remains as one of the main challenges in this area. Motivated by the need of faster and more accurate hyperparameter optimization algorithms we developed HyperBRKGA, a new population-based approach for hyperparameter optimization. HyperBRKGA combines the Biased Random Key Genetic Algorithm with an Exploitation Method in order to search the hyperparameter space more efficiently than other commonly used hyperparameter optimization algorithms, such as Grid Search, Random Search, CMA-ES or Bayesian Optimization. We develop and test two different alternatives for this Exploitation Method: Random Walk and Bayesian Walk. We also discuss and implement other schemes, such as a Training Data Reduction Strategy and a Diversity Control strategy, in order to further improve the efficacy of our method. We performed several computational experiments on 8 different datasets to assess the effectiveness of the proposed approach. Results showed that HyperBRKGA could find hyperparameter configurations that outperformed in terms of predictive quality the baseline methods in 6 out of 8 datasets while showing a reasonable execution time. Lastly, we conducted an ablation study and showed that the addition of every component was relevant to achieving high quality results.

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