Multi-objective optimization under uncertainty
published: Nov. 17, 2017, recorded: October 2017, views: 16
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Multi-objective optimization problems under uncertainty (MOPs-U) have received considerable and increasing interest these recent years in the field of discrete and continuous multi-objective optimization. MOPs-U arise in many important decision making problems in various sciences and industries and pose challenges for both engineers and researchers. The sources of uncertainty in MOPs-U are due to many factors such as environment parameters, decision variables and objectives functions. In this talk, we consider both random and epistemic models of uncertainty in the design od multi-objective evolutionary algorithms
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !