Parallel Problem Solving from Nature (PPSN'24)
Many members of the MALEO group participated in the 2024 edition of the Parallel Problem Solving from Nature (PPSN), a biannual major conference on advances in bio-inpsired optimisation and heuristics. Find more information on the official conference website.
Our PhD student Jeroen Rook from Twente University gave a tutorial on Advanced Use of Automatic Algorithm Configuration: Single- and Multi-Objective Approaches together with Manuel López-Ibáñez from the University of Manchester (UK).
Abstract: Most, if not all, optimisation algorithms have exposed parameters and various components, while the choice of which affects the performance of the algorithm. Finding the right values of these parameters that maximise the performance is a challenging task itself. Even more so, when this is done in a manual iterative manner, which leads to a tedious and suboptimal workflow. Fortunately, there exist several configuration frameworks that automatically search for a good configuration of parameters for the task at hand, optimising specific performance measures. These automated algorithm configurators have demonstrated to be a powerful tool in the design and configuration of algorithms in the last two decades. Although the concept of automated algorithm configuration (AAC) is generally known to algorithm designers and practitioners, the adoption of these methods in empirical research studies stays behind. One reason for this limited adoption is that most AAC frameworks are presented for the general use case of finding an optimally configured algorithm for a specific problem domain and do not explicitly showcase their usability in a broader setting. In this tutorial we go beyond the default AAC scenario to demonstrate the potential and capabilities of several AAC techniques in an actual research context. We especially focus on scenarios with multiple objectives involved, both at an algorithm level (configuring multi-objective optimisers) as at the configuration level (generating optimal performance trade-offs).
Download slides (in PDF)
