Denoising Autoencoders for fast Combinatorial Black Box Optimization
This work addresses optimization problems with low but non-negligible fitness evaluation costs, offering a faster method for domain-specific applications, though it is incremental as it builds on existing EDA frameworks.
The paper tackles combinatorial black box optimization by integrating Denoising Autoencoders into Estimation of Distribution Algorithms, resulting in DAE-EDA being considerably faster than BOA and RBM-EDA, sometimes by orders of magnitude, though with a higher number of fitness evaluations than BOA.
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Autoencoders (AE) are generative stochastic networks with these desired properties. We integrate a special type of AE, the Denoising Autoencoder (DAE), into an EDA and evaluate the performance of DAE-EDA on several combinatorial optimization problems with a single objective. We asses the number of fitness evaluations as well as the required CPU times. We compare the results to the performance to the Bayesian Optimization Algorithm (BOA) and RBM-EDA, another EDA which is based on a generative neural network which has proven competitive with BOA. For the considered problem instances, DAE-EDA is considerably faster than BOA and RBM-EDA, sometimes by orders of magnitude. The number of fitness evaluations is higher than for BOA, but competitive with RBM-EDA. These results show that DAEs can be useful tools for problems with low but non-negligible fitness evaluation costs.