A genetic algorithm is a computational approach inspired by the process of natural selection in biology. It is used to solve complex optimization problems by mimicking the principle of survival of the fittest. The process begins with the initialization of a population of potential solutions, referred to as individuals, represented by a set of genes or parameters. These individuals are then evaluated based on their fitness or suitability for solving the problem at hand. The algorithm then applies genetic operators such as selection, crossover, and mutation to generate new offspring individuals. This population evolves over multiple generations, with fitter individuals more likely to be selected and pass on their genes to the next generation. Gradually, the population converges towards increasingly optimal solutions, until a stopping condition is met, or the best solution is found. By iteratively exploring the search space, genetic algorithms provide a systematic and efficient way to find near-optimal or optimal solutions to a wide range of optimization problems.