Genetic Algorithms - A Brief Study

Genetic algorithms (GAs) are a powerful optimization technique inspired by the principles of natural selection and genetics.This paper provides a comprehensive overview of the various types of GAs, including basic, steady -state, and hybrid models, and explores their diverse applications in fields such as neural network optimization, travelling salesman problem, portfolio optimization, electrical circuit design, scheduling, and drug design.By leveraging evolutionary processes, GAs have demonstrated their ability to effectively navigate complex search spaces and identify optimal solutions.However, their implementation presents several challenges, including premature convergence, computational intensity, and the need for careful parameter tuning.Through a synthesis of foundational works by Holland (1975), Goldberg (1989), and contemporary studies by Deb (2001) and Michalewicz (1996), this paper highlights both the strengths and limitations of GAs.It underscores the potential for future advancements in addressing current challenges, thereby enhancing the efficiency and applicability of genetic algorithms in solving increasingly complex real -world problems.