The performance and sustain ability of modern organizations heavily rely on knowledge. In order to be able to manage it, organizations use dedicated tools - knowledge management systems, which enhance knowledge-related processes. The use of this kind of systems can imply a high number of resources and therefore a comprehensive analysis on the performance of the system from the organizational requirements perspective is needed. This article proposes a performance evaluation method based on genetic algorithm and Monte Carlo simulation combining an optimization-based approach with a probabilistic perspective. The main advantage of this method is that it considers a set of very specific requirements or needs, making it a highly customizable tool.

Knowledge Management System Evaluation Using Genetic Algorithm and Monte Carlo Method

Genetic Algorithm-Based Hyperparameter Optimization for Convolutional Neural Networks in the Classification of Crop Pests

The Internet of Things (IoT) connects numerous sensor nodes and devices, resulting in an increase in the bandwidth and data rates. However, this has led to a surge in data-hungry applications, which consume significant energy at battery-limited IoT nodes, causing rapid battery drainage. As a result, it is imperative to find a reliable solution that reduces the power consumption. A power optimization model utilizing a modified genetic algorithm is proposed to manage power resources efficiently and reduce high power consumption. In this model, each access point computes the optimal power using the modified genetic algorithm until it meets the fitness criteria and assigns it to each cellular user. Additionally, a weight-based user-scheduling algorithm is proposed to enhance network efficiency. This algorithm considers both the distance and received signal strength indicator (RSSI) to select a user for a specific base station. Furthermore, it assigns appropriate weights for the distance, and the RSSI helps increase the spectral efficiency performance. In this paper, the user-scheduling algorithm was assigned equal weights and combined with the power optimization model to analyze the power consumption and spectral efficiency performance metrics. The results demonstrated that the weight-based user-scheduling algorithm performed better and was supported by the optimal allocation of weights using a modified genetic algorithm. The outcome proved that the optimal allocation of transmission power for users reduced the cellular users’ power consumption and improved the spectral efficiency.

https://www.mdpi.com/2079-9292/12/8/1795/pdf?version=1681269249

Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm

Accurately predicting power consumption is essential to ensure a safe power supply. Various technologies have been studied to predict power consumption, but the prediction of power consumption using deep learning models has been quite successful. However, in order to predict power consumption by utilizing deep learning models, it is necessary to find an appropriate set of hyper-parameters. This introduces the problem of complexity and wide search areas. The power consumption field should be accurately predicted in various distributed areas. To this end, a customized consumption prediction deep learning model is needed, which is essential for optimizing the hyper-parameters that are suitable for the environment. However, typical deep learning model users lack the knowledge needed to find the optimal values of parameters. To solve this problem, we propose a method for finding the optimal values of parameters for learning. In addition, the layer parameters of deep learning models are optimized by applying genetic algorithms. In this paper, we propose a hyper-parameter optimization method that solves the time and cost problems that depend on existing methods or experiences. We derive a hyper-parameter optimization plan that solves the existing method or experience-dependent time and cost problems. As a result, the RNN model achieved a 30% and 21% better mean squared error and mean absolute error, respectively, than did the arbitrary deep learning model, and the LSTM model was able to achieve 9% and 5% higher performance.

/pdf/genetic-algorithm-for-the-optimization-of-a-building-power-7z37em58.pdf

Genetic Algorithm for the Optimization of a Building Power Consumption Prediction Model

Utilizing pre-trained models involves fully or partially using pre-trained parameters as initialization. In general, configuring a pre-trained model demands practitioners’ knowledge about problems or an exhaustive trial–error experiment according to a given task. In this paper, we propose tuning trainable layers using a genetic algorithm on a pre-trained model that is fine-tuned on single-channel image datasets for a classification task. The single-channel dataset comprises images from grayscale and preprocessed audio signals transformed into a log-Mel spectrogram. Four deep-learning models used in the experimental evaluation employed the pre-trained model with the ImageNet dataset. The proposed genetic algorithm was applied to find the highest fitness for every generation to determine the selective layer tuning of the pre-trained models. Compared to the conventional fine-tuning method and random layer search, our proposed selective layer search with a genetic algorithm achieves higher accuracy, on average, by 9.7% and 1.88% (MNIST-Fashion), 1.31% and 1.14% (UrbanSound8k), and 2.2% and 0.29% (HospitalAlarmSound), respectively. In addition, our searching method can naturally be applied to various datasets of the same task without prior knowledge about the dataset of interest.

/pdf/selective-layer-tuning-and-performance-study-of-pre-trained-17ctskw9.pdf

Yoona Choi

Papers

Genetic Algorithm for the Optimization of a Building Power Consumption Prediction Model

Selective Layer Tuning and Performance Study of Pre-Trained Models Using Genetic Algorithm