Multiobjective optimization using evolutionary algorithms

By Ivo F. Sbalzariniy, Sibylle Mullery, Petros Koumoutsakosyz. 2000

839 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5f4fcd905aee67b85f9cd992531cd16274203dc1

How researchers see this paper

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

1. Yaochu Jin
2. Ye Tian
3. Xing-yi Zhang
4. Ran Cheng
5. Kalyanmoy Deb
6. Qingfu Zhang
7. Handing Wang
8. Yanan Sun

Citations in the corpus

• Approximating the Set of Pareto-Optimal Solutions in Both the Decision and Objective Spaces by an Estimation of Distribution Algorithm (2009, background): “The image of the PS on the objective space, PF = {y ∈ Rm |y = F(x), x ∈ PS}, is called the Pareto front (PF) [1], [2].”
• A faster algorithm for calculating hypervolume (2006, background): “The most natural base case for HSO is when the points are reduced to one objective, when there can be only one nondominated point left in each slice.”
• A fast and elitist multiobjective genetic algorithm: NSGA-II (2002, methodology): “Many performance metrics have been suggested [1], [8], [24].”