Effects of experience bias when seeding with prior results

Abstract- 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 learningexperience bias produced by the previously evolved solutions.

• Type: Conference
• Year: 2005
• Where: CEC
• Authors: Mitchell A. Potter, R. Paul Wiegand, H. Joseph Blumenthal, Donald Sofge
• Keywords: Artificial Intelligence, CEC 2005, Evolutionary Algorithm, Machine Learning Perspective, Reasonable Similarity Metrics