Human readable genetic rules for scene boundary detection

Genetic programming is based on the Darwinian evolutionary theory that suggests that the best solution for a problem can be evolved by populating the solution space with an initial number of possible solutions and then evolving the solutions by means of mutation, reproduction and crossover until a candidate solution can be found that is close to or is the optimal solution for the problem. The initial solutions are randomly generated and set to a certain population size that the system can compute efficiently. Research has shown that better solutions can be obtained if 1) the population size is increased and 2) if multiple runs are performed of each experiment. If multiple runs are initiated on many machines the probability of finding an optimal solution are increased exponentially and computed more efficiently.