Many-Objective Evolutionary Algorithms

By Bingdong Li, Jinlong Li, Ke Tang, Xin Yao. ACM Computing Surveys, 2015

509 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/4b622e1a967bd043f8057028e59bad40de61c60a

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. Yaochu Jin
2. Xing-yi Zhang
3. Ye Tian
4. Ran Cheng
5. Xin Yao
6. Shengxiang Yang
7. Qingfu Zhang
8. Markus Olhofer

In Management Science and Operations Research

1. Yaochu Jin
2. Xing-yi Zhang
3. Ye Tian
4. Ran Cheng
5. Xin Yao
6. Shengxiang Yang
7. André H. Deutz
8. Qingfu Zhang

In Renewable Energy, Sustainability and the Environment

1. Ye Tian
2. Yaochu Jin
3. Xing-yi Zhang

Citations in the corpus

• A review on genetic algorithm: past, present, and future (2020, background): “The main motive of multiobjective GA is to generate the optimal Pareto Front in the objective space in such a way that no further enhancement in any fitness function without disturbing the other fitness functions [123].”
• A tutorial on multiobjective optimization: fundamentals and evolutionary methods (2018, background): “More-over, there is the convention to call problems with large m , not multiobjective optimization problems but many-objective optimization problems (see Fleming et al. 2005; Li et al. 2015).”
• Push and Pull Search for Solving Constrained Multi-objective Optimization Problems (2017, background): “In recent years, many work has been done in the field of many-objective evolutionary algorithms (MaOEAs) [14], which gives us a new way to solve CMOPs.”
• A practical guide to multi-objective reinforcement learning and planning (2021, background): “…the task of handling problems with many objectives (usually defined as four or more objectives) has emerged as a distinct sub-field, in recognition that algorithms which work well for a small number of objectives may scale poorly to many objectives [Von Lücken et al., 2014, Li et al., 2015].”
• Artificial intelligence in recommender systems (2020, background): “Multi-objective EAs that have more than three objectives are also termed many-objective EAs [71].”
• A benchmark test suite for evolutionary many-objective optimization (2017, background): “Inrecentyears,anumberofnewalgorithmshavebeenpro-posed for dealing with MaOPs [1], including the convergence enhancement based algorithms such as the grid-dominance-based evolutionary algorithm (GrEA) [2], the knee point-driven evolutionary algorithm (KnEA) [3], the two-archive algorithm (Two_Arch2)…”