Optimizing Software Performance and Bug Detection: Genetic Algorithm-Enhanced Time Convolution Neural Networks (GA-TCN)

EasyChair Preprint no. 12812

Abstract

In the realm of software development, optimizing performance and detecting bugs are crucial tasks for ensuring robust and reliable applications. This paper introduces a novel approach, termed Genetic Algorithm-Enhanced Time Convolution Neural Networks (GA-TCN), designed to address these challenges. The integration of genetic algorithms (GA) with time convolution neural networks (TCN) offers a powerful paradigm for technological evaluation and software bug training. By leveraging GA's evolutionary principles and TCN's ability to capture temporal dependencies, GA-TCN provides a versatile framework for enhancing software performance and detecting bugs. This paper presents the conceptual foundation, implementation methodology, and experimental results of GA-TCN, demonstrating its efficacy in various software development scenarios.

Keyphrases: bug detection, Genetic Algorithms, Performance enhancement, software optimization, Technological evaluation, Time convolution neural networks

@Booklet{EasyChair:12812,
  author = {Haney Zaki},
  title = {Optimizing Software Performance and Bug Detection: Genetic Algorithm-Enhanced Time Convolution Neural Networks (GA-TCN)},
  howpublished = {EasyChair Preprint no. 12812},

  year = {EasyChair, 2024}}