Scalability of using Restricted Boltzmann Machines for Combinatorial Optimization
This work addresses scalability issues in combinatorial optimization for researchers and practitioners, though it is incremental as it builds on existing EDA and RBM methods.
The paper tackled the challenge of scaling Estimation of Distribution Algorithms (EDAs) for combinatorial optimization by integrating Restricted Boltzmann Machines (RBMs) as generative models, resulting in RBM-EDA outperforming the state-of-the-art Bayesian Optimization Algorithm in CPU time, especially for large or complex problems, with less time required for model building.
Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Restricted Boltzmann Machines (RBMs) are generative neural networks with these desired properties. We integrate an RBM into an EDA and evaluate the performance of this system in solving combinatorial optimization problems with a single objective. We assess how the number of fitness evaluations and the CPU time scale with problem size and with problem complexity. The results are compared to the Bayesian Optimization Algorithm, a state-of-the-art EDA. Although RBM-EDA requires larger population sizes and a larger number of fitness evaluations, it outperforms BOA in terms of CPU times, in particular if the problem is large or complex. RBM-EDA requires less time for model building than BOA. These results highlight the potential of using generative neural networks for combinatorial optimization.