Averaging efficiently in the presence of noise
In this paper the problem of averaging because of noise on the fitness function is addressed. In simulations noise is mostly restricted to the finite precision of the numbers and can often be neglected. However in biology fluctuations are ubiquitous and also in real world applications, where evolutionary methods are used as optimization tools, the presence of noise has to be coped with . This article originated from the second point: Optimizing the structure of Neural Networks their fitness is the result of a learning process. This value depends on the stochastic initialization of the connection strengths and thus represents a noisy fitness value. To reduce noise one can average over several evaluations per individual which is costly. The aim of this work is to introduce a method to reduce the number of evaluations per individual.
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