An overview on evolutionary algorithms for many‐objective optimization problems
Author:
Affiliation:
1. Facultad Politécnica Universidad Nacional de Asunción San Lorenzo Paraguay
2. CICESE Research Center Computer Science Department, Applied Physics Division, Ensenada Mexico
Publisher
Wiley
Subject
General Computer Science
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/widm.1267
Reference58 articles.
1. An Empirical Assessment of the Properties of Inverted Generational Distance on Multi- and Many-Objective Optimization
2. Brockhoff D. Friedrich T. Hebbinghaus N. Klein C. Neumann F. &Zitzler E. (2007). Do additional objectives make a problem harder? InProceedings of the 9th Annual Conference on Genetic and Evolutionary Computation(pp. 765–772). London UK: ACM Press.https://doi.org/10.1145/1276958.1277114
3. A Multiobjective Evolutionary Algorithm Using Gaussian Process-Based Inverse Modeling
4. A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization
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