Classes Scheduler sing Genetic Algorithm

Abstract

In the ever-evolving of software development, the surge in artificial intelligence (AI) and deep learning technologies has been nothing short of astonishing. As the world bears witness to this transformative era, staying abreast of cutting-edge problem-solving methodologies becomes paramount. To master these techniques, practical experimentation is essential. Among these techniques, the genetic algorithm stands as a venerable tool for addressing complex problems. Its longevity in the field attests to its efficacy. Genetic algorithms find their niche in scenarios that demand iterative sorting under diverse and multifaceted conditions. Whether optimizing schedules, streamlining resource allocation, or enhancing decision-making processes, these algorithms offer a powerful framework. Their versatility extends across various domains, making them excellent tools for modern problem solvers. The study of genetic algorithms involves practical validation. By subjecting them to rigorous testing and empirical study, to uncover their limits, capabilities, and potential applications. As delve deeper into this field, novel solutions will be unlocked and pave the way for further advancements. The primary focus of this research paper is to demonstrate genetic algorithm principles, specifically in the context of optimizing timetable schedules through the use of Evolutionary Algorithms (EAs). The study proceeds by presenting a developed (.NET) application that employs the prescribed methodology of numerous genetic algorithms to seek the most optimal solution for scheduling college classes, subject to multiple constraints. Subsequently, a comparative analysis of the results obtained from two selected algorithms is conducted.