Towards a generic control strategy for Evolutionary Algorithms: an adaptive fuzzy-learning approach

— This paper presents a new method to generalize strategies in order to control parameters of Evolutionary Algorithms (EAs). A learning process establishes the relationship between optimal quality parameters and diversity, and simplifies control to just one variable, highly correlated with Exploration/Exploitation Balance, in such way that strategies can ned in more abstract terms. The acquired knowledge is expressed in a simple fashion that helps the user to understand internal mechanics of EA. The model is built after a careful example gathering and encoded in Fuzzy Logic Controllers.

• Type: Conference
• Year: 2007
• Where: CEC
• Authors: Jorge Maturana, Frédéric Saubion
• Keywords: Artificial Intelligence, Careful Example Gathering, CEC 2007, Fuzzy Logic Controllers, Optimal Quality Parameters