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Updated: Jul 10, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
Comparison-based algorithms are robust and randomized algorithms are anytime
Sylvain Gelly1, Sylvie Ruette, Olivier Teytaud
1Equipe TAO (INRIA Futurs), LRI, UMR 8623 (CNRS - Université Paris-Sud), bat. 490 Université Paris-Sud 91405 Orsay Cedex, France. gelly@lri.fr
Abstract:
Randomized search heuristics (e.g., evolutionary algorithms, simulated annealing etc.) are very appealing to practitioners, they are easy to implement and usually provide good performance. The theoretical analysis of these algorithms usually focuses on convergence rates. This paper presents a mathematical study of randomized search heuristics which use comparison based selection mechanism. The two main results are that comparison-based algorithms are the best algorithms for some robustness criteria and that introducing randomness in the choice of offspring improves the anytime behavior of the algorithm. An original Estimation of Distribution Algorithm combining both results is proposed and successfully experimented.
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