Empirical algorithmics

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Emirical algorithmics (sometimes also called experimental algorithmics) is the area within computer science that uses empirical methods to study the behaviour of algorithms.

Methods from empirical algorithmics complement theoretical methods for the analysis of algorithms and, by using principled empirical methods particularly from statistics, can yield insights into the behaviour of algorithms that are (currently) inaccessible to theoretical analysis. They can also be used to achieve substantial improvements in the performance of algorithms.

There are two main branches of empirical algorithmics: empirical methods for analysing and characterising the behaviour of algorithm and empirical methods for improving the performance of algorithms. The former uses mostly techniques and tools from statistics, while the latter is based on approaches from statistics, machine learning and optimization.

Research in empirical algorithmics is published in several journals, including the ACM Journal on Experimental Algorithmics. Algorithms] (JEA) and the Journal of Artificial Intelligence Research (JAIR), as well as at numerous conferences, including SEA, WEA, AAAI, IJCAI, CP and SLS.

Well-known researchers in empirical algorithmics include Marco Chiarandini, Catherine McGeoch, Holger H. Hoos, David S. Johnson, Kevin Leyton-Brown, Ruben Ruiz and Thomas Stützle.