Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
By D. V. Veldhuizen, Gary B. Lamont. Evolutionary Computation, 2000
1,330 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics
View Paper: https://www.semanticscholar.org/paper/6f1ccafe065e29b0951202168d7a256c81b25850
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
- Yaochu Jin
- Kalyanmoy Deb
- Hisao Ishibuchi
- Qingfu Zhang
- Ran Cheng
- Carlos Artemio Coello Coello
- Kay Chen Tan
- Xing-yi Zhang
In Computational Theory and Mathematics
- Yaochu Jin
- Kalyanmoy Deb
- Hisao Ishibuchi
- Qingfu Zhang
- Ran Cheng
- Carlos Artemio Coello Coello
- Kay Chen Tan
- Xing-yi Zhang
Citations in the corpus
- The Measure of Pareto Optima (2003, background): “, [7, 8]) although some have been based on SA [9].”
- Optimum dimension of geometric parameters of solar chimney power plants – A multi-objective optimization approach (2014, background): “These objectives are characterized by distinct measures of performance that may be (in) dependent and/or incommensurable (Veldhuizen and Lamont, 2000; Konak et al., 2006).”
- Three-objective genetics-based machine learning for linguistic rule extraction (2001, background): “For reviews of multi-objective genetic algorithms, see [36] [41] [42] .”
- Finding Knees in Multi-objective Optimization (2004, methodology): “Following a classification by Veldhuizen [16], the articulation of preferences may be done either before (a priori), during (progressive), or after (a posteriori) the optimization process.”
- MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition (2007, background): “Arguably, if there is a fitness assignment scheme for assigning an individual solution a relative fitness value to reflect its utility for selection, then scalar optimization EAs can be readily extended for dealing with MOPs, although other techniques such as mating restriction [20], diversity maint”
- Abandoning Objectives: Evolution Through the Search for Novelty Alone (2011, methodology): “In addition to single-objective optimization, there also exist evolutionary methods that aim to optimize several objectives at once: multi-objective evolutionary algorithms (MOEAs; Veldhuizen and Lamont, 2000).”
- Statistical strategies for avoiding false discoveries in metabolomics and related experiments (2007, background): “This is an important field that is not really the subject of this review, but we do consider it useful to mention a few works on the subject (Ringuest, 1992; Dasgupta et al., 1999; Zitzler, 1999; Van Veldhuizen and Lamont, 2000; Deb, 2001; Coello Coello et al., 2002; Handl and Knowles, 2004, 2006a; ”