Multi-level ranking for constrained multi-objective evolutionary optimisation
Document Type
Conference Proceeding
Publisher
Springer, Berlin, Heidelberg
Faculty
Faculty of Computing, Health and Science
School
School of Computer and Information Science
RAS ID
4819
Abstract
In real-world optimisation problems, feasibility of solutions is invariably an essential requirement. A natural way to deal with feasibility is to cast it as an additional objective in a multi-objective optimisation setting. In this paper, we consider two possible ways to do this, using a multi-level scheme for ranking solutions. One strategy considers feasibility first, before considering objective values, while the other reverses this ordering. The first strategy has been explored before, while the second has not. Experiments show that the second strategy can be much more successful on some difficult problems.
DOI
10.1007/11844297_57
Comments
Hingston, P., Barone, L., Huband, S., & While, L. (2006). Multi-level ranking for constrained multi-objective evolutionary optimisation. In Parallel Problem Solving from Nature-PPSN IX (pp. 563-572). Springer, Berlin, Heidelberg. Available here