Abstract
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 ℝn 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.
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Joshi, R., Deshpande, B. (2013). Scalability of Population-Based Search Heuristics for Many-Objective Optimization. In: Esparcia-Alcázar, A.I. (eds) Applications of Evolutionary Computation. EvoApplications 2013. Lecture Notes in Computer Science, vol 7835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37192-9_48
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DOI: https://doi.org/10.1007/978-3-642-37192-9_48
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