Abstract
Measuring the performance of algorithms over dynamic optimization problems (DOPs) presents some important differences when compared to static ones. One of the main problems is the loss of solution quality as the optimization process advances in time. The objective in DOPs is tracking the optima as the landscape changes; however it is possible that the algorithm fails to follow the optima after some changes happened. The main goal in this article is to introduce a new way of measuring how algorithms are able to maintain their performance during the dynamic optimization process. We propose a measure based on linear regression and study its behaviour. In order to do so, we propose a scenario based on the moving peaks benchmark and analyze our results using several metrics existing in the literature. We test our measure for degradation on the same scenario, applying it over accuracy values obtained for each period, and obtain results which help us to explain changes in algorithm performances.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
