@article {pub3769,
	title = {Harmless Overfitting: Using Denoising Autoencoders in Estimation of Distribution Algorithms},
	author = {Malte Probst },
	year = {2020},
	month = {June},
	abstract = {Estimation of Distribution Algorithms (EDAs) are metaheuristics where learning a model
and sampling new solutions replaces the variation operators recombination and mutation
used in standard Genetic Algorithms. The choice of these models as well as the corresponding
training processes are subject to the bias/variance tradeoff, also known as under- and
overfitting: simple models cannot capture complex interactions between problem variables,
whereas complex models are susceptible to modeling random noise. This paper suggests
using Denoising Autoencoders (DAEs) as generative models within EDAs (DAE-EDA).
The resulting DAE-EDA is able to model complex probability distributions. Furthermore,
overfitting is less harmful, since DAEs overfit by learning the identity function. This overfitting
behavior introduces unbiased random noise into the samples, which is no major
problem for the EDA but just leads to higher population diversity. As a result, DAE-EDA
runs for more generations before convergence and searches promising parts of the solution
space more thoroughly. We study the performance of DAE-EDA on several combinatorial
single-objective optimization problems. In comparison to the Bayesian Optimization
Algorithm, DAE-EDA requires a similar number of fitness evaluations but is much faster
and can be parallelized efficiently, making it the preferred choice especially for large and
difficult optimization problems.},
	publisher = {jmlr.org},
	journal = {Journal of Machine Learning Research},
	editor = {Francis Bach},
	volume = {21},
	number = {78},
	pages = {1-31}
}