Name
Abstract | ||
|---|---|---|
Assuming that evolutionary multiobjective optimization (EMO) mainly deals with set problems, one can identify three core questions in this area of research: 1) how to formalize what type of Pareto set approximation is sought; 2) how to use this information within an algorithm to efficiently search for a good Pareto set approximation; and 3) how to compare the Pareto set approximations generated by different optimizers with respect to the formalized optimization goal. There is a vast amount of studies addressing these issues from different angles, but so far only a few studies can be found that consider all questions under one roof. This paper is an attempt to summarize recent developments in the EMO field within a unifying theory of set-based multiobjective search. It discusses how preference relations on sets can be formally defined, gives examples for selected user preferences, and proposes a general preferenceindependent hill climber for multiobjective optimization with theoretical convergence properties. Furthermore, it shows how to use set preference relations for statistical performance assessment and provides corresponding experimental results. The proposed methodology brings together preference articulation, algorithm design, and performance assessment under one framework and thereby opens up a new perspective on EMO. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/TEVC.2009.2016569 | IEEE Trans. Evolutionary Computation |
Keywords | Field | DocType |
set problem,set-based multiobjective optimization,emo field,selected user preference,preference relation,good pareto,evolutionary multiobjective optimization,formalized optimization goal,set-based multiobjective search,multiobjective optimization,preference articulation,pareto optimization,convergence,evolutionary algorithm,algorithm design and analysis,statistical analysis,evolutionary algorithms,set theory,computer networks,approximation algorithms,multi objective optimization,algorithm design,evolutionary computation | Approximation algorithm,Hill climbing,Set theory,Mathematical optimization,Search algorithm,Evolutionary algorithm,Evolutionary computation,Multi-objective optimization,Artificial intelligence,Mathematics,Machine learning,Pareto principle | Journal |
Volume | Issue | ISSN |
14 | 1 | 1089-778X |
Citations | PageRank | References |
66 | 2.39 | 28 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eckart Zitzler | 1 | 4678 | 291.01 |
| Lothar Thiele | 2 | 14025 | 957.82 |
| Johannes Bader | 3 | 694 | 18.64 |