Urban Škvorc

• Master of Science
• PhD Student at Jožef Stefan Institute

In optimization, algorithm selection, which is the selection of the most suitable algorithm for a specific problem, is of great importance, as algorithm performance is heavily dependent on the problem being solved. However, when using machine learning for algorithm selection, the performance of the algorithm selection model depends on the data used...

When designing a benchmark problem set, it is important to create a set of benchmark problems that are a good generalization of the set of all possible problems. One possible way of easing this difficult task is by using artificially generated problems. In this paper, one such single-objective continuous problem generation approach is analyzed and...

To visually present the overall performance of several algorithms tested on several benchmark problems on one plot, we present a machine learning approach, called performViz. It allows one to clearly see, from a single plot, which algorithms are most suited for a given problem, the influence of each problem on the overall algorithm performance and...

The performance measures and statistical techniques selected affect the conclusions we can draw on the behavior of the algorithms. For this reason, we propose more robust performance statistics for addressing statistical and practical significance, as well as investigating the exploration and exploitation powers of stochastic optimization algorithm...

Selecting the relevant algorithm for a given problem is a crucial first step for achieving good optimization algorithm performance. Exploratory Landscape Analysis can help with this problem by calculating landscape features that numerically describe individual problems. To understand the problem space in single-objective numerical optimization, we...

In benchmarking theory, creating a comprehensive and uniformly distributed set of problems is a crucial first step to designing a good benchmark. However, this step is also one of the hardest, as it can be difficult to determine how to evaluate the quality of the chosen problem set. In this article, we evaluate if the field of exploratory landscape...

The GECCO Workshop on Real-Parameter Black-Box Optimization Benchmarking Series is a series of benchmarking workshops held every year since 2009 that evaluates the performance of new optimization algorithms. Originally, the workshop organizers provided results for every year the workshop took place. In this article, we directly compare algorithms f...

The Special Sessions and Competitions on Real-Parameter Single Objective Optimization are benchmarking competitions held every year since 2013 that are used to evaluate the performance of new optimization algorithms. One flaw of these competitions is that algorithms are compared only to other algorithms submitted in the same year, not with algori...