Black-box optimization (BBO) problems arise in numerous scientic and engineering applications and are characterized by compu- tationally intensive objective functions, which severely limit the number of evaluations that can be performed. We present a robust set of features that analyze the tness landscape of BBO problems and show how an algorithm portfolio approach can exploit these general, problem indepen- dent features and outperform the utilization of any single minimization search strategy. We test our methodology on data from the GECCO Workshop on BBO Benchmarking 2012, which contains 21 state-of-the- art solvers run on 24 well-established functions.
Lecture Notes in Computer Science, 2013, p. 30-36
Main Research Area:
Lecture Notes in Computer Science
Learning and Intelligent OptimizatioN Conference 2013