Optimizing Solutions through Genetic Algorithms: A Review of Contemporary Approaches

Abstract

Genetic Algorithms (GAs) have emerged as a powerful optimization technique inspired by the process of natural selection. They are extensively utilized in various domains, from engineering to computer science, to solve complex problems. This review article delves into the fundamental principles and recent advancements in genetic algorithms. We discuss the iterative process of selection, crossover, and mutation, which mimics biological evolution. Additionally, the role of fitness functions in guiding the search for optimal solutions is explored.