Enhancing Technological Evaluation via Genetic Algorithm-Empowered Time Convolution Neural Networks (GA-TCN): a Novel Approach for Optimized Analysis

EasyChair Preprint 12811

Abstract

In the rapidly evolving landscape of technological assessment, the integration of advanced computational techniques has become paramount for accurate and efficient analysis. This paper proposes a novel approach, termed Genetic Algorithm-empowered Time Convolution Neural Networks (GA-TCN), designed to enhance technological evaluation through optimized analysis. The GA-TCN framework synergistically combines the adaptability of genetic algorithms with the temporal modeling capabilities of time convolution neural networks, aiming to provide a robust methodology for extracting valuable insights from complex technological datasets. By leveraging the evolutionary principles of genetic algorithms, the GA-TCN system dynamically adjusts network architectures and parameters, optimizing model performance and adaptability to diverse technological contexts. Furthermore, the incorporation of time convolutional layers enables the model to capture temporal dependencies and patterns inherent in time-series data, facilitating more accurate predictions and assessments of technological trends and behaviors. Through comprehensive experimentation and evaluation on real-world datasets, the efficacy of the GATCN approach is demonstrated, showcasing its superior performance in comparison to traditional methodologies.

Keyphrases: Genetic Algorithm, Optimization, Technological evaluation, Time Convolution Neural Network, Time series data, analysis, temporal modeling

@booklet{EasyChair:12811,
  author    = {Asad Ali},
  title     = {Enhancing Technological Evaluation via Genetic Algorithm-Empowered Time Convolution Neural Networks (GA-TCN): a Novel Approach for Optimized Analysis},
  howpublished = {EasyChair Preprint 12811},
  year      = {EasyChair, 2024}}