Guidance in evolutionary multi-objective optimization

By J. Branke, T. Kaußler, Hartmut Schmeck. 2001

310 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/ff18c417fa5816906dacd6f5b452c4803ebf5ef6

How researchers see this paper

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

In Artificial Intelligence

1. Yaochu Jin
2. Ran Cheng
3. Xin Yao
4. Hisao Ishibuchi
5. Xing-yi Zhang
6. Kalyanmoy Deb
7. Ye Tian
8. Yusuke Nojima

In Computational Theory and Mathematics

1. Yaochu Jin
2. Ran Cheng
3. Kalyanmoy Deb
4. Xin Yao
5. Xing-yi Zhang
6. Hisao Ishibuchi
7. Ye Tian
8. Yusuke Nojima

In Management Science and Operations Research

1. Yaochu Jin
2. Kalyanmoy Deb
3. Xin Yao
4. Hisao Ishibuchi
5. Xing-yi Zhang
6. Ye Tian
7. Yusuke Nojima
8. Ke Tang

Citations in the corpus

• A survey on multi-objective evolutionary algorithms for many-objective problems (2014, methodology): “In [11], Branke et al. proposed the Guided Multi-Objective Evolutionary Algorithm (G-MOEA) which, as the method proposed by Sato et al. [87], is based on modifying the dominated region of each solution.”
• Many-Objective Evolutionary Algorithms (2015, methodology): “An identical idea is used in the guided multiobjective evolutionary algorithm (G-MOEA), although explained from the perspective of integrating the user’s preference [Branke et al. 2001].”
• A Preference-Based Evolutionary Algorithm for Multi-Objective Optimization (2009, methodology): “In the guided multi-objective evolutionary algorithm (G-MOEA) proposed by Branke et al. (2001) user preferences were taken into account using trade-offs, supplied by the DM, to modify the definition of dominance.”
• Multiple Criteria Decision Making, Multiattribute Utility Theory: Recent Accomplishments and What Lies Ahead (2008, unknown): “Representative references on EMO are provided by Corne et al. (2000), Branke et al. (2001), Zitzler et al. (2001), Deb (2001, 2003), Fonseca et al. (2003) Machine learning and knowledge discovery techniques have entered the field of preference modeling , mainly as a result of the concept of…”
• Many-Objective Optimization: An Engineering Design Perspective (2005, background): “Branke et al. [ 2 ] describe the Guided Multi-Objective Evolutionary Algorithm, where DM preferences are manifested through a modification of the dominance definition, which specifies the level of trade-offs acceptable among objectives, i.e.”
• Evolutionary many-objective optimization: A short review (2008, background): “[36], the modification of Pareto dominance has a similar effect on multiobjective evolution as the modification of objective functions such as (7).”
• Reference point based multi-objective optimization using evolutionary algorithms (2006, methodology): “[11], user preferences were taken into account by modifying the definition of dominance.”
• Preference-Inspired Coevolutionary Algorithms for Many-Objective Optimization (2013, methodology): “In [11], five different scalability improvement approaches are considered: 1) modification of the density estimator [12], [13]; 2) introduction of different ranks [13]–[15]; 3) modification of Pareto-dominance [16], [17]; 4) modification of objective functions [17], [18]; and 5) hybridization with l”
• An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints (2014, methodology): “Finally, NSGA-III is applied to two practical problems, nine-objective problems in Section VII.”
• Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems (2015, background): “Since most of the current multi-objective algorithms in the literature are posteriori , the coverage and number of solutions are very important for decision making after the optimization process [45].”