A genetic algorithm is a computational approach inspired by the theory of natural selection and evolution. It is used to solve complex optimization and search problems. The algorithm creates a population of potential solutions, represented as sets of parameters or chromosomes. These solutions undergo a process of selection, crossover, and mutation, mimicking the mechanisms of genetic reproduction and variation. The fittest individuals are more likely to survive and reproduce, passing their traits to the next generation. This iterative process continues until an optimal solution or a satisfactory result is found. Genetic algorithms are particularly effective in finding solutions to problems with numerous variables and constraints, where traditional mathematical approaches may struggle.