This paper presents results of the BBOB-2009 benchmarking of 31 search algorithms on 24 noiseless functions in a black-box optimization scenario in continuous domain. The runtime of the algorithms, measured in number of function evaluations, is investigated and a connection between a single convergence graph and the runtime distribution is uncovered. Performance is investigated for different dimensions up to 40-D, for different target precision values, and in different subgroups of functions. Searching in larger dimension and multi-modal functions appears to be more difficult. The choice of the best algorithm also depends remarkably on the available budget of function evaluations.

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This paper presents the result for Simultaneous Perturbation Stochastic Approximation (SPSA) on the BBOB 2010 noisy testbed. SPSA is a stochastic gradient approximation strategy which uses random directions for the gradient estimate. The paper describes the steps performed by the strategy and the experimental setup. The chosen setup represents a rather basic variant of SPSA. The strategy can successfully solve 5 functions for D = 2, 2 for D = 3, and 1 for D = 5. For each function at least one target level is reached up to D = 3.

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