The complexity of continuous optimisation by comparison-based algorithms has been developed in several recent papers. Roughly speaking, these papers conclude that a precision ∈ can be reached with cost Θ(n log(1/ε)) in dimension n within polylogarithmic factors for the sphere function. Compared to other (non comparison-based) algorithms, this rate is not excellent; on the other hand, it is classically considered that comparison-based algorithms have some robustness advantages, as well as scalability on parallel machines and simplicity. In the present paper we show another advantage, namely resilience to useless variables, thanks to a complexity bound Θ(m log(1/ε)) where m is the codimension of the set of optima, possibly m n. In addition, experiments show that some evolutionary algorithms have a negligible computational complexity even in high dimension, making them practical for huge problems with many useless variables.
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