Bayesian approach to global optimization and application to multiobjective and constrained problems
Journal of Optimization Theory and Applications
Employing linear regression in regression tree leaves
ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
A simple regression based heuristic for learning model trees
Intelligent Data Analysis
Meta-Modeling in Multiobjective Optimization
Multiobjective Optimization
Sequential model-based optimization for general algorithm configuration
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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Multi-objective optimization problems are usually solved with evolutionary algorithms when the objective functions are cheap to compute, or with surrogate-based optimizers otherwise. In the latter case, the objective functions are modeled with powerful non-linear model learners such as Gaussian Processes or Support Vector Machines, for which the training time can be prohibitively large when dealing with optimization problems with moderately expensive objective functions. In this paper, we investigate the use of model trees as an alternative kind of model, providing a good compromise between high expressiveness and low training time. We propose a fast surrogate-based optimizer exploiting the structure of model trees for candidate selection. The empirical results show the promise of the approach for problems on which classical surrogate-based optimizers are painfully slow.