What technique is used in genetic algorithms to find solutions to problems?

Genetic algorithms, inspired by the principles of natural selection and genetics, utilize a process of natural evolution to find solutions to problems. This technique mimics how organisms evolve over generations, where the fittest individuals are selected for reproduction in order to produce the next generation. In a genetic algorithm, potential solutions to a problem are encoded as individuals (often represented as strings of data), and these individuals undergo processes similar to biological evolution, including selection, crossover (recombination), and mutation.

Through repeated iterations of these processes, genetic algorithms explore the solution space, gradually improving the quality of solutions based on their performance against a defined fitness function. This approach allows genetic algorithms to efficiently search large and complex problem spaces, making them powerful tools for optimization problems where traditional methods may fall short.