Pareto-Front Exploration with Uncertain Objectives

By Jürgen Teich. International Conference on Evolutionary Multi-Criterion Optimization, 2001

295 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/8a6362118d04661d2d5b7ef82d148fdc72d18897

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Himanshu Gupta
2. Kalyanmoy Deb
3. Yaochu Jin
4. Carlos Artemio Coello Coello
5. Ran Cheng
6. Xing-yi Zhang
7. Gary G. Yen
8. Qingfu Zhang

In Control and Systems Engineering

1. Himanshu Gupta
2. Kalyanmoy Deb

In Statistics and Probability

1. Kalyanmoy Deb
2. Himanshu Gupta

Citations in the corpus

• Searching for Robust Pareto-Optimal Solutions in Multi-objective Optimization (2005, unknown): “Teich [6] extended Paretodominance for handling uncertain objectives and Hughes [7] computed the error estimate for using deterministic Pareto-dominance in noisy functions.”
• Introducing Robustness in Multi-Objective Optimization (2006, methodology): “In the context of multi-objective optimization, Teich (1998) handled errors in modeling objective functions by modifying the domination operator.”
• Preference-based Pareto optimization in certain and noisy environments (2009, unknown): “Hughes (2001) and Teich (2001) both proposed a modification of the ranking procedure of MOEAs.”
• A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems (2011, unknown): “These mechanisms are based on the use of either precedence operators between imprecise values [106] [108] [154] or EMO algorithms capable of optimizing a mix of crisp and fuzzy objectives [144].”