Genetic algorithms is a class of evolutionary computation methods employed for solving optimization and search problems. These algorithms simulate biological evolutionary processes such as selection, mutation, and crossover to iteratively generate and improve a population of candidate solutions, exploring complex search spaces to converge towards optimal or near-optimal outcomes. Their significance lies in their robust performance on problems where traditional gradient-based techniques are inapplicable or computationally infeasible, offering a powerful heuristic approach for diverse applications in computer science, engineering, and artificial intelligence.