Abstract
This work proposes an adaptive ∈-ranking method to enhance Pareto based selection, aiming to develop effective many objective evolutionary optimization algorithm. ∈-ranking fine grains ranking of solutions after they have been ranked by Pareto dominance, using a randomized sampling procedure combined with ∈-dominance to favir a good distribution of the samples. In essence, sampled solutions keep their initial rank and solutions located within the virtually expanded dominance reions of the sampled solutions are demoted to an inferior rank. The parameter ∈ that determines the expanded regions of dominance of the sampled solutions is adapted to each generation so that the number of highest ranked solutions is kept close to a desired number expressed as a fraction of the population size. We enhanced NSGA-II with the proposed method and verify its performance on MNK-Landscapes. Experimented results show that the adaptive method works effectively and that convergence and diversity of the solutions found can improve remarkably on MNK-Landscapes with 3 ≤ M ≤ 10 objectives.
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.
