SOURCE-LINKED INTELLIGENCE

Dynamic Selection and Configuration of Black-box Optimization Algorithms

CORDIS · observation · Publication date unknown

handled rather naively in practice. To obtain our dynamic approaches, we intertwine insights about black-box optimization algorithms, obtained through rigorous theoretical analyses, with automated machine learning techniques. In particular, we will design trajectory-based algorithm selection and configuration techniques that combine exploratory landscape analysis with newly designed algorithm features that capture information about the solver-instance interaction. We compare the efficiency of these feature-based approaches with deep learning techniques, reinforcement learning, and approaches based on hyperparameter optimization. We will further increase our project's impact by validating its results on applications in bio-medicine and in computational mechanics." Black-box Optimization, Randomized Search Heuristics, Evolutionary Computation, Automated Machine Learning, Algorithm Selection, Algorithm Configuration, Local Search, Discrete Optimization, Algorithm Analysis, Randomized Algorithms, Parameter Control

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recordType

award

status

SIGNED

region

EU

value

1999975

unit

EUR