Optimisation problems are at the core of operations research, and we often employ mathematical solvers to find optimal solutions. However, while these solvers excel in accuracy, they can be slow. Therefore, metaheuristic techniques have been developed to provide near-optimal solutions with a boost in speed. In this presentation, the speaker will introduce genetic algorithm, an example of a nature-inspired metaheuristic, and demonstrate how to build it in Python. As an evolutionary algorithm, genetic algorithm can be used as an artificial intelligence technique for solving complex optimization and search problems.