Real-time algorithm configuration

This dissertation presents a number of contributions to the field of algorithm configur- ation. In particular, we present an extension to the algorithm configuration problem, real-time algorithm configuration, where configuration occurs online on a stream of instances, without the need for prior training, and problem solutions are returned in the shortest time possible. We propose a framework for solving the real-time algorithm configuration problem, ReACT. With ReACT we demonstrate that by using the parallel computing architectures, commonplace in many systems today, and a robust aggregate ranking system, configuration can occur without any impact on performance from the perspective of the user. This is achieved by means of a racing procedure. We show two concrete instantiations of the framework, and show them to be on a par with or even exceed the state-of-the-art in offline algorithm configuration using empirical evaluations on a range of combinatorial problems from the literature. We discuss, assess, and provide justification for each of the components used in our framework instantiations. Specifically, we show that the TrueSkill ranking system commonly used to rank players’ skill in multiplayer games can be used to accurately es- timate the quality of an algorithm’s configuration using only censored results from races between algorithm configurations. We confirm that the order that problem instances arrive in influences the configuration performance and that the optimal selection of configurations to participate in races is dependent on the distribution of the incoming in- stance stream. We outline how to maintain a pool of quality configurations by removing underperforming configurations, and techniques to generate replacement configurations with minimal computational overhead. Finally, we show that the configuration space can be reduced using feature selection techniques from the machine learning literature, and that doing so can provide a boost in configuration performance

Candidate selection and instance ordering for realtime algorithm configuration

Many modern combinatorial solvers have a variety of parameters through which a user can customise their behaviour. Algorithm configuration is the process of selecting good values for these parameters in order to improve performance. Time and again algorithm configuration has been shown to significantly improve the performance of many algorithms for solving challenging computational problems. Automated systems for tuning parameters regularly out-perform human experts, sometimes but orders of magnitude. Online algorithm configurators, such as ReACTR, are able to tune a solver online without incurring costly offline training. As such ReACTR’s main focus is on runtime minimisation while solving combinatorial problems. To do this ReACTR adopts a one-pass methodology where each instance in a stream of instances to be solved is considered only as it arrives. As such ReACTR’s performance is sensitive to the order in which instances arrive. It is still not understood which instance orderings positively or negatively effect the performance of ReACTR. This paper investigates the effect of instance ordering and grouping by empirically evaluating different instance orderings based on difficulty and feature values. Though the end use is generally unable to control the order in which instances arrive it is important to understand which orderings impact Re- ACTR’s performance and to what extent. This study also has practical benefit as such orderings can occur organically. For example as business grows the problems it may encounter, such as routing or scheduling, often grow in size and difficulty. ReACTR’s performance also depends strongly configuration selection procedure used. This component controls which configurations are selected to run in parallel from the internal configuration pool. This paper evaluates various ranking mechanisms and different ways of combining them to better understand how the candidate selection procedure affects realtime algorithm configuration. We show that certain selection procedures are superior to others and that the order which instances arrive in determines which selection procedure performs best. We find that both instance order and grouping can significantly affect the overall solving time of the online automatic algorithm configurator ReACTR. One of the more surprising discoveries is that having groupings of similar instances can actually negatively impact on the overall performance of the configurator. In particular we show that orderings based on nearly any instance feature values can lead to significant reductions in total runtime over random instance orderings. In addition, certain candidate selection procedures are more suited to certain orderings than others and selecting the correct one can show a marked improvement in solving times

Online Search Algorithm Configuration

This paper outlines an online approach for algorithm configuration which uses the power of modern multicore system to evaluate multiple parameters configurations in parallel

ReACT: Real-Time Algorithm Configuration through Tournaments

The success or failure of a solver is oftentimes closely tied to the proper configuration of the solver's parameters. However, tuning such parameters by hand requires expert knowledge, is time consuming, and is error-prone. In recent years, automatic algorithm configuration tools have made significant advances and can nearly always find better parameters than those found through hand tuning. However, current approaches require significant offline computational resources, and follow a train-once methodology that is unable to later adapt to changes in the type of problem solved. To this end, this paper presents Real-time Algorithm Configuration through Tournaments (ReACT), a method that does not require any offline training to perform algorithm configuration. ReACT exploits the multi-core infrastructure available on most modern machines to create a system that continuously searches for improving parameterizations, while guaranteeing a particular level of performance. The experimental results show that, despite the simplicity of the approach, ReACT quickly finds a set of parameters that is better than the default parameters and is competitive with state-of-the-art algorithm configurators

Nucleoid-associated protein HU controls three regulons that coordinate virulence, response to stress and general physiology in Salmonella enterica serovar Typhimurium

The role of the HU nucleoid-associated proteins in gene regulation was examined in Salmonella enterica serovar Typhimurium. The dimeric HU protein consists of different combinations of its alpha and beta subunits. Transcriptomic analysis was performed with cultures growing at 37 degrees C at 1, 4 and 6 h after inoculation with mutants that lack combinations of HU alpha and HU beta. Distinct but overlapping patterns of gene expression were detected at each time point for each of the three mutants, revealing not one but three regulons of genes controlled by the HU proteins. Mutations in the hup genes altered the expression of regulatory and structural genes in both the SPI1 and SPI2 pathogenicity islands. The hupA hupB double mutant was defective in invasion of epithelial cell lines and in its ability to survive in macrophages. The double mutant also had defective swarming activity and a competitive fitness disadvantage compared with the wild-type. In contrast, inactivation of just the hupB gene resulted in increased fitness and correlated with the upregulation of members of the RpoS regulon in exponential-phase cultures. Our data show that HU coordinates the expression of genes involved in central metabolism and virulence and contributes to the success of S. eat erica as a pathogen