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Published in:
2013 | OriginalPaper | Chapter
Scalability of Population-Based Search Heuristics for Many-Objective Optimization
Authors : Ramprasad Joshi, Bharat Deshpande
Published in: Applications of Evolutionary Computation
Publisher: Springer Berlin Heidelberg
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Beginning with Talagrand [16]’s seminal work, isoperimetric inequalities have been used extensively in analysing randomized algorithms. We develop similar inequalities and apply them to analysing population-based randomized search heuristics for multiobjective optimization in ℝ
n
space. We demonstrate the utility of the framework in explaining an empirical observation so far not explained analytically: the curse of dimensionality, for many-objective problems. The framework makes use of the black-box model now popular in EC research.