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We propose a novel surrogate-assisted Evolutionary Algorithm for solvingexpensive combinatorial optimization problems. We integrate a surrogate model,which is used for fitness value estimation, into a state-of-the-art P3-likevariant of the Gene-Pool Optimal Mixing Algorithm (GOMEA) and adapt theresulting algorithm for solving non-binary combinatorial problems. We test theproposed algorithm on an ensemble learning problem. Ensembling several modelsis a common Machine Learning technique to achieve better performance. Weconsider ensembles of several models trained on disjoint subsets of a dataset.Finding the best dataset partitioning is naturally a combinatorial non-binaryoptimization problem. Fitness function evaluations can be extremely expensiveif complex models, such as Deep Neural Networks, are used as learners in anensemble. Therefore, the number of fitness function evaluations is typicallylimited, necessitating expensive optimization techniques. In our experiments weuse five classification datasets from the OpenML-CC18 benchmark andSupport-vector Machines as learners in an ensemble. The proposed algorithmdemonstrates better performance than alternative approaches, including Bayesianoptimization algorithms. It manages to find better solutions using just severalthousand fitness function evaluations for an ensemble learning problem with upto 500 variables.

A Novel Surrogate-assisted Evolutionary Algorithm Applied to Partition-based Ensemble Learning