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

• URL: http://arxiv.org/abs/2104.08048v1
• Date: Fri, 16 Apr 2021 11:51:18 GMT
• Title: A Novel Surrogate-assisted Evolutionary Algorithm Applied to Partition-based Ensemble Learning
• Authors: Arkadiy Dushatskiy, Tanja Alderliesten, Peter A. N. Bosman
• Abstract summary: We propose a novel surrogate-assisted Algorithm for solving expensive optimization problems. We integrate a surrogate model, which is used for fitness value estimation, into a state-of-the-art P3-like variant of the Evolutionary Gene- Evolutionary Optimal Mixing Algorithm. We test the proposed algorithm on an ensemble learning problem.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel surrogate-assisted Evolutionary Algorithm for solving expensive combinatorial optimization problems. We integrate a surrogate model, which is used for fitness value estimation, into a state-of-the-art P3-like variant of the Gene-Pool Optimal Mixing Algorithm (GOMEA) and adapt the resulting algorithm for solving non-binary combinatorial problems. We test the proposed algorithm on an ensemble learning problem. Ensembling several models is a common Machine Learning technique to achieve better performance. We consider ensembles of several models trained on disjoint subsets of a dataset. Finding the best dataset partitioning is naturally a combinatorial non-binary optimization problem. Fitness function evaluations can be extremely expensive if complex models, such as Deep Neural Networks, are used as learners in an ensemble. Therefore, the number of fitness function evaluations is typically limited, necessitating expensive optimization techniques. In our experiments we use five classification datasets from the OpenML-CC18 benchmark and Support-vector Machines as learners in an ensemble. The proposed algorithm demonstrates better performance than alternative approaches, including Bayesian optimization algorithms. It manages to find better solutions using just several thousand fitness function evaluations for an ensemble learning problem with up to 500 variables.

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