Abstract:
Evolutionary algorithms are generally believed to perform
well in the presence of noise. Thus, they are often used for optimization
in noisy environments. It comes as a surprise that hardly more than a handful
of studies has dealt with the question of just how well they are doing
and what can be done to improve their performance. The present paper presents
empirical results regarding the behavior of genetic algorithms an evolution
strategies in the presence of fitness noise for a range of objective funnctions.
Bounds for the mean residual location error and the stationary fitness
error for optimization of general quadratic fitness models with evolution
strategies are derived and compared with measurements.
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