Definitely not an expert here, but the main difference is that in machine learning the goal is to find/optimize algorithm to fit given datasets, whereas in gene

Definitely not an expert here, but the main difference is that in machine learning the goal is to find/optimize algorithm to fit given datasets, whereas in genetic programming the goal is to find/optimize dataset to fit given algorithm. There is a lot of overlap though. EDIT: > what are the most suitable problems that Genetic Programming is to solve Given above, genetic algorithms are really suitable when part of your problem domain is bounded execution time. You perform n iterations of genetic algorithm and even if the result does not fit within fitness function, you still get some result that is closer to fitness function than a wild guess.