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Noise is a common factor in most real-world optimization problems.
Sources of noise can include physical measurement limitations,
stochastic simulation models, incomplete sampling of large spaces,
and human-computer interaction. Evolutionary algorithms are
general, nature-inspired heuristics for numerical search and
optimization that are frequently observed to be particularly robust
with regard to the effects of noise. Noisy Optimization with
Evolution Strategies contributes to the understanding of
evolutionary optimization in the presence of noise by investigating
the performance of evolution strategies, a type of evolutionary
algorithm frequently employed for solving real-valued optimization
problems. By considering simple noisy environments, results are
obtained that describe how the performance of the strategies scales
with both parameters of the problem and of the strategies
considered. Such scaling laws allow for comparisons of different
strategy variants, for tuning evolution strategies for maximum
performance, and they offer insights and an understanding of the
behavior of the strategies that go beyond what can be learned from
mere experimentation. This first comprehensive work on noisy
optimization with evolution strategies investigates the effects of
systematic fitness overvaluation, the benefits of distributed
populations, and the potential of genetic repair for optimization
in the presence of noise. The relative robustness of evolution
strategies is confirmed in a comparison with other direct search
algorithms. Noisy Optimization with Evolution Strategies is an
invaluable resource for researchers and practitioners of
evolutionary algorithms.
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