Genetic programming in the context of machine learning. Genetic programming is a technique used in evolutionary computation to evolve programs or models to solve a specific problem. In the context of machine learning, it involves using genetic algorithms to evolve a population of programs or models over multiple generations to find the best solution to a particular task.
Genetic programming starts with an initial population of randomly generated programs or models. These programs are then evaluated based on a fitness function that measures how well they perform on the given task. The programs with higher fitness scores are selected to “reproduce,” passing on their genetic information to create new programs for the next generation. This process mimics the principles of natural selection, where only the fittest individuals survive and reproduce.
Through repeated generations of selection, crossover (combining genetic information from different programs), and mutation (introducing small changes to programs), the population evolves to create increasingly better solutions to the problem. Genetic programming can be used for a variety of tasks, such as regression, classification, and even designing neural network architectures.