  Algorithm portfolios represent a strategy of composing multiple heuristic
algorithms, each suited to a different class of problems, within a single
general solver that will choose the best suited algorithm for each input. This
approach recently gained popularity especially for solving combinatoric
problems, but optimization applications are still emerging. The COCO platform
of the BBOB workshop series is the current standard way to measure performance
of continuous black-box optimization algorithms.
  As an extension to the COCO platform, we present the Python-based COCOpf
framework that allows composing portfolios of optimization algorithms and
running experiments with different selection strategies. In our framework, we
focus on black-box algorithm portfolio and online adaptive selection. As a
demonstration, we measure the performance of stock SciPy optimization
algorithms and the popular CMA algorithm alone and in a portfolio with two
simple selection strategies. We confirm that even a naive selection strategy
can provide improved performance across problem classes.
