The identification of performance-optimizing parameter settings is an important part of the development and application of algorithms. We describe an automatic framework for this algorithm configuration problem. More formally, we provide methods for optimizing a target algorithm’s performance on a given class of problem instances by varying a set of ordinal and/or categorical parameters. We review a family of local-search-based algorithm configuration procedures and present novel techniques for accelerating them by adaptively limiting the time spent for evaluating individual configurations. We describe the results of a comprehensive experimental evaluation of our methods, based on the configuration of prominent complete and incomplete algorithms for SAT. We also present what is, to our knowledge, the first published work on automatically configuring the CPLEX mixed integer programming solver. All the algorithms we considered had default parameter settings that were manually identified with considerable effort. Nevertheless, using our automated algorithm configuration procedures, we achieved substantial and consistent performance improvements. 1.

High-performance algorithms play an important role in many areas of computer science and are core components of many software systems used in real-world applications. Traditionally, the creation of these algorithms requires considerable expertise and experience, often in combination with a substantial amount of trial and error. Here, we outline a new approach to the process of designing high-performance algorithms that is based on the use of automated procedures for exploring potentially very large spaces of candidate designs. We contrast this computer-aided design approach with the traditional approach and discuss why it can be expected to yield better performing, yet simpler algorithms. Finally, we sketch out the high-level design of a software environment that supports our new design approach. Existing work on algorithm portfolios, algorithm selection, algorithm configuration and parameter tuning, but also on general methods for discrete and continuous optimisation methods fits naturally into our design approach and can be integrated into the proposed software environment. 1

The best-performing algorithms for many hard problems are highly parameterized. Selecting the best heuristics and tuning their parameters for optimal overall performance is often a difficult, tedious, and unsatisfying task. This thesis studies the automation of this important part of algorithm design: the configuration of discrete algorithm components and their continuous parameters to construct an algorithm with desirable empirical performance characteristics. Automated configuration procedures can facilitate algorithm development and be applied on the end user side to optimize performance for new instance types and optimization objectives. The use of such procedures separates high-level cognitive tasks carried out by humans from tedious low-level tasks that can be left to machines. We introduce two alternative algorithm configuration frameworks: iterated local search in parameter configuration space and sequential optimization based on response surface models. To the best of our knowledge, our local search approach is the first that goes beyond local optima. Our model-based search techniques significantly outperform existing techniques and extend them in ways crucial for general algorithm configuration: they can handle categorical parameters, optimization objectives defined across multiple instances, and tens of thousands