Title

Multi-level ranking for constrained multi-objective evolutionary optimisation

Document Type

Conference Proceeding

Publisher

Springer, Berlin, Heidelberg

Faculty

Computing, Health and Science

School

Computer and Information Science

RAS ID

4819

Comments

Originally published as: 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. Original article available here

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

 
COinS
 

Link to publisher version (DOI)

10.1007/11844297_57