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Many difficult problems can be viewed as search problems. However, given a new task with an embedded search problem, it is challenging to state and find a truly effective search approach. In this paper, we address the complex task of signal processing optimization. We first introduce and discuss the complexities of this domain. In general, a single signal processing algorithm can be represented by a very large number of different but mathematically equivalent formulas. When these formulas are implemented in actual code, unfortunately their running times differ significantly. Signal processing algorithm optimization aims at finding the fastest formula. We present a new approach that successfully solves this problem, using an evolutionary stochastic search algorithm, STEER, to search through the very large space of formulas. We empirically compare STEER against other search methods, showing that it notably can find faster formulas while still only timing a very small portion of the search space.Keywords: Search/Learning for optimization