Landscape-Aware Fixed-Budget Performance Regression and Algorithm Selection for Modular CMA-ES Variants

• URL: http://arxiv.org/abs/2006.09855v1
• Date: Wed, 17 Jun 2020 13:34:57 GMT
• Title: Landscape-Aware Fixed-Budget Performance Regression and Algorithm Selection for Modular CMA-ES Variants
• Authors: Anja Jankovic and Carola Doerr
• Abstract summary: We show that it is possible to achieve high-quality performance predictions with off-the-shelf supervised learning approaches. We test this approach on a portfolio of very similar algorithms, which we choose from the family of modular CMA-ES algorithms.
• Score: 1.0965065178451106
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated algorithm selection promises to support the user in the decisive task of selecting a most suitable algorithm for a given problem. A common component of these machine-trained techniques are regression models which predict the performance of a given algorithm on a previously unseen problem instance. In the context of numerical black-box optimization, such regression models typically build on exploratory landscape analysis (ELA), which quantifies several characteristics of the problem. These measures can be used to train a supervised performance regression model. First steps towards ELA-based performance regression have been made in the context of a fixed-target setting. In many applications, however, the user needs to select an algorithm that performs best within a given budget of function evaluations. Adopting this fixed-budget setting, we demonstrate that it is possible to achieve high-quality performance predictions with off-the-shelf supervised learning approaches, by suitably combining two differently trained regression models. We test this approach on a very challenging problem: algorithm selection on a portfolio of very similar algorithms, which we choose from the family of modular CMA-ES algorithms.

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