Objective Reduction in Many-Objective Optimization: Linear and Nonlinear Algorithms

The difficulties faced by existing multiobjective evolutionary algorithms (MOEAs) in handling many-objective problems relate to the inefficiency of selection operators, high computational cost, and difficulty in visualization of objective space. While many approaches aim to counter these difficultie...

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Bibliographic Details
Published in:	IEEE transactions on evolutionary computation Vol. 17; no. 1; pp. 77 - 99
Main Authors:	Saxena, D. K., Duro, J. A., Tiwari, A., Deb, K., Qingfu Zhang
Format:	Journal Article
Language:	English
Published:	New York, NY IEEE 01.02.2013 Institute of Electrical and Electronics Engineers
Subjects:	Algorithm design and analysis Algorithmics. Computability. Computer arithmetics Applied sciences Computational efficiency Computer science; control theory; systems Data processing. List processing. Character string processing Decision making Decision theory. Utility theory Evolutionary multiobjective optimization Exact sciences and technology many-objective optimization Mathematical programming maximum variance unfolding and kernels Memory organisation. Data processing Operational research and scientific management Operational research. Management science Optical fibers Optimization principal component analysis Redundancy Search problems Software Theoretical computing Fidelity Visualization Costs Error estimation Evolutionary algorithm Variability Multiobjective programming Non linear programming Optimization Relevance maximum variance unfolding and kernels Dependence Robustness Productivity Sensitivity analysis Dominating set Pareto optimum Preservation Decision making Redundancy Critical parameter Dominance Linear programming many-objective optimization Standards Dimension reduction Quality control Algorithm analysis Principal component analysis Evolutionary multiobjective optimization
ISSN:	1089-778X, 1941-0026
Online Access:	Get full text
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