Effects of experience bias when seeding with prior results

Seeding the population of an evolutionary algorithm with solutions from previous runs has proved to be useful when learning control strategies for agents operating in a complex, changing environment. It has generally been assumed that initializing a learning algorithm with previously learned solutions will be helpful if the new problem is similar to the old. We will show that this assumption sometimes does not hold for many reasonable similarity metrics. Using a more traditional machine learning perspective, we explain why seeding is sometimes not helpful by looking at the learning-experience bias produced by the previously evolved solutions.