In many areas of AI, there are different algorithms to solve the same type of problem. Often, these algorithms are complementary in the sense that one algorithm works well when others fail and vice versa. For example in propositional satisfiability solving (SAT), there are complete tree-based solvers aimed at structured, industrial-like problems, and local search solvers aimed at randomly generated problems. In many practical cases, the performance difference between algorithms can be very large, for example as shown by Xu et al. (2012) for SAT. Unfortunately, the correct selection of an algorithm is not always as easy as described above and even easy decisions require substantial expert knowledge about algorithms and the problem instances at hand.
Per-instance algorithm selection (Rice, 1976) is a way to leverage this complementarity between different algorithms. Instead of running a single algorithm, a portfolio (Huberman et al., 1997; Gomes and Selman, 2001) consisting of several complementary algorithms is employed together with a learned selector. The selector automatically chooses the best algorithm from the portfolio for each instance to be solved.
Formally, the task is to select the best algorithm from a portfolio of algorithms for a given instance from a set of instances with respect to a performance metric (e.g., runtime, error, solution quality or accuracy). To this end, an algorithm selection system learns a mapping from an instance to a selected algorithm such that the performance, measured as cost, across all instances is minimized (w.l.o.g.):
Algorithm selection has gained prominence in many areas and made tremendous progress in recent years. Algorithm selection systems established new state-of-the-art performance in several areas of AI, for example propositional satisfiability solving (Xu et al., 2008)111In propositional satisfiability solving (SAT), algorithm selection system were even banned from the SAT competition for some years, but are allowed in a special track now.2008; van Rijn et al., 2018), maximum satisfiability solving (Ansótegui et al., 2016), answer set programming (Lindauer et al., 2017a; Calimeri et al., 2017), constraint programming (Hurley et al., 2014; Amadini et al., 2014), and the traveling salesperson problem (Kotthoff et al., 2015). However, the multitude of different approaches and application domains makes it difficult to compare different algorithm selection systems, which presented users with a very practical meta-algorithm selection problem – which algorithm selection system should be used for a given task. The algorithm selection competitions can help users to make the decision which system and approach to use, based on a fair comparison across a diverse range of different domains.
The first step towards being able to perform such comparisons was the introduction of the Algorithm Selection Benchmark Library (ASlib, Bischl et al., 2016). ASlib consists of many algorithm selection scenarios for which performance data of all algorithms on all instances is available. These scenarios allow for fair and reproducible comparisons of different algorithm selection systems. ASlib enabled the competitions we report on here.
Structure of the paper
In this competition report, we summarize the results and insights gained by running two algorithm selection competitions based on ASlib. These competitions were organized in 2015 – the ICON Challenge on Algorithm Selection – and in 2017 – the Open Algorithm Selection Challenge.222This paper builds upon individual short papers for each competition (Kotthoff et al., 2017; Lindauer et al., 2017b) and presents a unified view with a discussion of the setups, results and lessons learned. We start by giving a brief background on algorithm selection (Section 2) and an overview on how we designed both competitions (Section 3). Afterwards we present the results of both competitions (Section 4) and discuss the insights obtained and open challenges in the field of algorithm selection, identified through the competitions (Section 5).
2 Background on Algorithm Selection
In this section, we discuss the importance of algorithm selection, several classes of algorithm selection methods and ways to evaluate algorithm selection problems.
2.1 Importance of Algorithm Selection
The impact of algorithm selection in several AI fields is best illustrated by the performance of such approaches in AI competitions. One of the first well-know algorithm selection systems was SATzilla (Xu et al., 2008), which won several first places in the SAT competition 2009 and the SAT challenge 2012. To refocus on core SAT solvers, portfolio solvers (including algorithm selection systems) were banned from the SAT competition for several years—now, they are allowed in a special track. In the answer set competition 2011, the algorithm selection system claspfolio (Hoos et al., 2014) won the NP-track and later in 2015, ME-ASP (Maratea et al., 2015) won the competition. In constraint programming, sunny-cp (Amadini et al., 2014) won the open track of the MiniZinc Challenge for several years (2015, 2016 & 2017). In AI planning, a simple static portfolio of planners (fast downward stone soup; Helmert et al., 2011) won a track at the International Planning Competition (IPC) in 2011. More recently, the online algorithm selection system Delfi (Katz et al., 2018) won a first place at IPC 2018. In QBF, an algorithm selection system called QBF Portfolio (Hoos et al., 2018) won third place at the prenex track of QBFEVAL 2018.
Algorithm selection does not only perform well for combinatorial problems, but it is also an important component in automated machine learning (AutoML) systems. For example, the AutoML system auto-sklearn uses algorithm selection to initialize its hyperparameter optimization(Feurer et al., 2015b) and won two AutoML challenges Feurer et al. (2018).
2.2 Algorithm Selection Approaches
Figure 1 shows a basic per-instance algorithm selection framework that is used in practice. A basic approach involves (i) representing a given instance with a vector of numerical features (e.g., number of variables and constraints of a CSP instance), (ii) inducing a selection machine learning model that selects an algorithm for the given instance based on its features . Generally, these machine learning models are induced based on a dataset with datapoints to map an input to output , which closely represents . In this setting,
is typically the vector of numerical featuresfrom some instance that has been observed before. There are various variations for representing the values and ways for algorithm selection system to leverage the predictions . We briefly review several classes of solutions.
that models the performance of individual algorithms in the portfolio. A regression model can be trained for each on with and for each previously observed instance that was ran on. The machine learning algorithm can then predict how well algorithm performs on a given instance from . The algorithm with the best predicted performance is selected for solving the instance (e.g., Horvitz et al., 2001; Xu et al., 2008).
- Combinations of unsupervised clustering and classification
that partitions instances into clusters based on the instance features , and determines the best algorithm for each cluster . Given a new instance , the instance features determine the nearest cluster w.r.t. some distance metric; the algorithm assigned to is applied (e.g., Ansótegui et al., 2009).
- Pairwise Classification
- Stacking of several approaches
2.3 Why is algorithm selection more than traditional machine learning?
In contrast to typical machine learning tasks, each instance has a weight attached to it. It is not be important to select the best algorithm on instances on which all algorithms perform nearly equally, but it is crucial to select the best algorithm on an instance on which all but one algorithm perform poorly (e.g., all but one time out). The potential gain from making the best decision can be seen as a weight for that particular instance.
Instead of predicting a single algorithm, schedules of algorithms can also be used. One variant of algorithm schedules (Kadioglu et al., 2011; Hoos et al., 2015) are static (instance-independent) pre-solving schedules which are applied before any instance features are computed (Xu et al., 2008). Computing the best-performing schedule is usually an NP-hard problem. Alternatively, a sequence of algorithms can be predicted for instance-specific schedules (Amadini et al., 2014; Lindauer et al., 2016).
Computing instance features can come with a large amount of overhead, and if the objective is to minimize runtime, this overhead should be minimized. For example, on industrial-like SAT instances, computing some instance features can take more than half of the total time budget.
2.4 Evaluation of Algorithm Selection Systems
The purpose of performing algorithm selection is to achieve performance better than any individual algorithm could. In many cases, overhead through the computation of the instance features used as input for the machine learning models is incurred. This diminishes performance gains achieved through selecting good algorithms and has to be taken into account for evaluating algorithm selection systems.
To be able to assess the performance gain of algorithm selection systems, two baselines are commonly compared against (Xu et al., 2012; Lindauer et al., 2015; Ansótegui et al., 2016): (i) the performance of the individual algorithm performing best on all training instances (called single best solver (SBS)), which denotes what can be achieved without algorithm selection; (ii) the performance of the virtual best solver (VBS) (also called oracle performance), which makes perfect decisions and chooses the best-performing algorithm on each instance without any overhead. The VBS corresponds to the overhead-free parallel portfolio that runs all algorithms in parallel and terminates as soon as the first algorithm finishes.
The performance of the baselines and of any algorithm selection system varies for different scenarios. We normalize the performance of an algorithm selection system on a given scenario by the performance of the SBS and VBS, as a cost to be minimized, and measure how much of the gap between the two it closed as follows:
where corresponds to perfect performance, equivalent to the VBS, and corresponds to the performance of the SBS.333In the 2017 competition, the gap was defined such that corresponded to VBS and to SBS. For consistency with the 2015 results, we use the metric as defined in Equation 2 here. The performance of an algorithm selection system will usually be between 0 and 1; if it is larger than 1 it means that simply always selecting the SBS is a better strategy.
A common way of measuring runtime performance is penalized average runtime (PAR10) (Hutter et al., 2014; Lindauer et al., 2015; Ansótegui et al., 2016): the average runtime across all instances, where algorithms are run with a timeout and penalized with a runtime ten times the timeout if they do not complete within the time limit.
3 Competition Setups
In this section, we discuss the setups of both competitions. Both competitions were based on ASlib, with submissions required to read the ASlib format as input.
3.1 General Setup: ASlib
Figure 2 shows the general structure of an ASlib scenario (Bischl et al., 2016). ASlib scenarios contain pre-computed performance values for all algorithms in a portfolio on a set of training instances (e.g., runtime for SAT instances or accuracy for Machine Learning datasets). In addition, a set of pre-computed instance features are available for each instance, as well as the time required to compute the feature values (the overhead). The corresponding task description provides further information, e.g., runtime cutoff, grouping of features, performance metric (runtime or solution quality) and indicates whether the performance metric is to be maximized or minimized. Finally, it contains a file describing the train-test splits. This file specifies which instances should be used for training the system (), and which should be used for evaluating the system ().
3.2 Competition 2015
In 2015, the competition asked for complete systems to be submitted which would be trained and evaluated by the organizers. This way, the general applicability of submissions was emphasized – rather than doing well only with specific models and after manual tweaks, submissions had to demonstrate that they can be used off-the-shelf to produce algorithm selection models with good performance. For this reason, submissions were required to be open source or free for academic use.
The scenarios used in 2015 are shown in Table 1. The competition used existing ASlib scenarios that were known to the participants beforehand. There was no secret test data in 2015; however, the splits into training and testing data were not known to participants. We note that these are all runtime scenarios, reflecting what was available in ASlib at the time.
Submissions were allowed to specify the feature groups and a single pre-solver for each ASlib scenario (a statically-defined algorithm to be run before any feature computation to avoid overheads on easy instances), and required to produce a list of the algorithms to run for each instance (each with an associated timeout). The training time for a submission was limited to CPU hours on each scenario; each submission had the same computational resources available and was executed on the same hardware. AutoFolio was the only submission that used the full hours. The submissions were evaluated on different train-test splits, to reduce the potential influence of randomness. We considered the three metrics mean PAR10 score, mean misclassification penalty (the additional time that was required to solve an instance compared to the best algorithm on that instance), and number of instances solved within the timeout. The final score was the average remaining gap (Equation 2) across these three metrics, the train-test splits, and the scenarios.
3.3 Competition 2017
Compared to 2015, we changed the setup of the competition in 2017 with the following goals in mind:
fewer restrictions on the submissions regarding computational resources and licensing;
better scaling of the organizational overhead to more submissions, in particular not having to run each submission manually;
more flexible schedules for computing features and running algorithms; and
a more diverse set of algorithm selection scenarios, including new scenarios.
To achieve the first and second goal, the participants did not submit their systems directly, but only the predictions made by their system for new test instances (using a single train-test split). Thus, also submissions from closed-source systems were possible, although all participants made their submissions open-source in the end. We provided full information, including algorithms’ performances, for a set of training instances, but only the feature values for the test instances to submitters. Participants could invest as many computational resources as they wanted to compute their predictions. While this may give an advantage to participants who have access to large amounts of computational resources, such a competition is typically won through better ideas and not through better computational resources. To facilitate easy submission of results, we did not run multiple train-test splits as in 2015. We be briefly investigated the effects of this in Section 5.4. We note that this setup is quite common in other machine learning competitions, e.g., the Kaggle competitions (Carpenter, 2011).
To support more complex algorithm selection approaches, the submitted predictions were allowed to be an arbitrary sequence of algorithms with timeouts and interleaved feature computations. Thus, any combination of these two components was possible (e.g., complex pre-solving schedules with interleaved feature computation). Complex pre-solving schedules were used by most submissions for scenarios with runtime as performance metric.
We collected several new algorithm selection benchmarks from different domains; out of the used scenarios were completely new and not disclosed to participants before the competition (see Table 2). We obfuscated the instance and algorithm names such that the participants were not able to easily recognize existing scenarios.
To show the impact of algorithm selection on the state of the art in different domains, we focused the search for new scenarios on recent competitions for CSP, MAXSAT, MIP, QBF, and SAT. Additionally, we developed an open-source Python tool that connects to OpenML (Vanschoren et al., 2014) and converts a Machine Learning study into an ASlib scenario.444See https://github.com/openml/openml-aslib.
To ensure diversity of the scenarios with respect to the application domains, we selected at most two scenarios from each domain to avoid any bias introduced by focusing on a single domain. In the 2015 competition, most of the scenarios came from SAT, which skewed the evaluation in favor of that. Finally, we also considered scenarios with solution quality as performance metric (instead of runtime) for the first time. The new scenarios were added to ASlib after the competition; thus the competition was not only enabled by ASlib, but furthers its expansion.
For a detailed description of the competition setup in 2017, we refer the interested reader to Lindauer et al. (2017b).
We now discuss the results of both competitions.
4.1 Competition 2015
The competition received a total of 8 submissions from 4 different groups of researchers comprising 15 people. Participants were based in 4 different countries on 2 continents. A provides an overview of all submissions.
Table 3 shows the final ranking. The zilla system is the overall winner, although the first- and second-placed entries are very close. All systems perform well on average, closing more than half of the gap between virtual and single best solver. Additionally, we show the normalized PAR10 score for comparison to the 2017 results, where only the PAR10 metric was used. Detailed results of all metrics (PAR10, misclassification penalty, and solved) are presented in D.
For comparison, we show three additional systems. Autofolio-48 is identical to
Autofolio (a submitted algorithm selector that searches over different selection approaches
and their hyperparameter settings (Lindauer et al., 2015)),
but was allowed hours training time (four times the default)
to assess the impact of additional tuning of hyperparameters.
LLAMA-regr are simple approaches based on the LLAMA algorithm selection toolkit (Kotthoff, 2013).555 Both LLAMA approaches use regression models to
predict the performance of each algorithm individually and
for each pair of algorithms to predict their performance difference.
Both approaches did not use pre-solvers and feature selection, both selected only a single algorithm, and
their hyperparameters were not tuned.
Both LLAMA approaches use regression models to predict the performance of each algorithm individually and for each pair of algorithms to predict their performance difference. Both approaches did not use pre-solvers and feature selection, both selected only a single algorithm, and their hyperparameters were not tuned.The relatively small difference between AutoFolio and AutoFolio-48 shows that allowing more training time does not increase performance significantly. The good ranking of the two simple LLAMA models shows that reasonable performance can be achieved even with simple off-the-shelf approaches without customization or tuning. Figure 3 (combined scores) and Figure 4 (PAR10 scores) show critical distance plots on the average ranks of the submissions. According to the Friedman test with post-hoc Nemenyi test, there is no statistically significant difference between any of the submissions.
More detailed results can be found in Kotthoff (2015).
4.2 Competition 2017
In 2017, there were submissions from groups. Similar to 2015, participants were based in 4 different countries on 2 continents. While most of the submissions came from participants of the 2015 competition, there were also submissions by researchers who did not participate in 2015.
|Rank||System||Avg. Gap||Avg. Rank|
Table 4 shows the results in terms of the gap metric (see Equation 2) based on PAR10, as well as the ranks; detailed results are in Table 9 (E). The competition was won by ASAP.v2, which obtained the highest scores on the gap metric both in terms of the average over all datasets, and the average rank across all scenarios. Both ASAP systems clearly outperformed all other participants on the quality scenarios. However, Sunny-fkvar did best on the runtime scenarios, followed by ASAP.v2.
Figure 5 shows critical distance plots on the average ranks of the submissions. There is no statistically significant difference between the best six submissions, but the difference to the worst submissions is statistically significant.
5 Open Challenges and Insights
In this section, we discuss insights and open challenges indicated by the results of the competitions.
5.1 Progress from 2015 to 2017
The progress of algorithm selection as a field from 2015 to 2017 seems to be rather small. In terms of the remaining gap between virtual best and single best solver, the results were nearly the same (the best system in 2015 achieved about in terms of PAR10, and the best system in 2017 about ). On the only scenario used in both competitions (SAT12-ALL), the performance stayed nearly constant. Nevertheless, the competition in 2017 was more challenging because of the new and more diverse scenarios. While the community succeeded in coming up with more challenging problems, there appears to be room for more innovative solutions.
5.2 Statistical Significance
Figures 3, 4 and 5 show ranked plots, with the critical distance required according to the Friedman with post-hoc Nemenyi test to assert statistical significant difference between multiple systems (Demsar, 2006). In the 2015 competition, none of the differences between the submitted systems were statistical significant, whereas in the 2017 competition only some differences where statistical significant.
Failure to detect a significant difference does not imply that there is no such difference: the statistical tests are based on a relatively low number of samples and thus have limited power.
Even though the statistical significance results should be interpreted with care, the critical difference plots are still informative. They show, e.g., that the systems submitted in the 2015 challenge were closer together (ranked approximately between and ) than the systems submitted in 2017 (ranked approximately between and ).
5.3 Robustness of Algorithm Selection Systems
As the results of both competitions show, choosing one of the state-of-the-art algorithm selection systems is still a much better choice than simply always using the single best algorithm. However, as different algorithm selection systems have different strengths, we are now confronted with a meta-algorithm selection problem – selecting the best algorithm selection approach for the task at hand. For example, while the best submission in 2017 achieved gap between SBS and VBS remaining, the virtual best selector over the portfolio of all submissions would have achieved . An open challenge is to develop such a meta-algorithm selection system, or a single algorithm selection system that performs well across a wide range of scenarios.
One step in this direction is the per-scenario customization of the systems, e.g., by using hyperparameter optimization methods (Gonard et al., 2017; Liu et al., 2017; Cameron et al., 2017), per-scenario model selection (Malone et al., 2017), or even per-scenario selection of the general approach combined with hyperparameter optimization (Lindauer et al., 2015). However, as the results show, more fine-tuning of an algorithm selection system does not always result in a better-performing system. In 2015, giving much more time to Autofolio resulted in only a very minor performance improvement, and in 2017 ASAP.v2 performed better than its refined successor ASAP.v3.
In addition to the general observations above, we note the following points regarding robustness of the submissions:
zilla performed very well on SAT scenarios (average rank: ) but only mediocre on other domains (average rank: out of submissions) in 2015;
ASAP won in 2017, but sunny-fkvar performed better on runtime scenarios;
both CSP scenarios in 2017 were very similar (same algorithm portfolio, same instances, same instance features) but the performance metric was changed (one scenario with runtime and one scenario with solution quality). On the runtime scenario, Sunny-fkvar performed very well, but on the quality scenario ASAP.v3/2 performed much better.
5.4 Impact of Randomness
One of the main differences between the 2015 and 2017 challenges was that in 2015, the submissions were evaluated on 10 cross-validation splits to determine the final ranking, whereas in 2017, only a single training-test split was used. While this greatly reduced the effort for the competition organizers, it increased the risk of a particular submission with randomized components getting lucky.
In general, our expectation for the performance of a submission is that it does not depend on randomness much, i.e., its performance does not vary significantly across different test sets or random seeds. On the other hand, as we observed in Section 5.3, achieving good performance across multiple scenarios is an issue.
To determine the effect of randomness on performance, we ran the competition winner, ASAP.v2, with different random seeds on the CSP-Minizinc-Obj-2016 (Camilla) scenario, where it performed particularly well. Figure 6
shows the cumulative distribution function of the performance across different random seeds. The probability of ASAP.v2 performing as good or better than it did is very low, suggesting that it did choose a lucky random seed.
This result demonstrates the importance of evaluating algorithm selection systems across multiple random seeds, or multiple test sets. If we replace ASAP’s obtained score with the median score of the CDF shown in Figure 6, it would have ranked at third place.
5.5 Hyperparameter Optimization
All systems submitted to either of the competitions leverage a machine learning model that predicts the performance of algorithms. It is well known that hyperparameter optimization is important to get well-performing machine learning models (see, e.g., Snoek et al. (2012); Thornton et al. (2013); van Rijn and Hutter (2018)). Nevertheless, not all submissions optimized hyperparameters, e.g., the winner in 2017 ASAP.v2 (Gonard et al., 2017)
used the default hyperparameters of its random forest. Given previous results byLindauer et al. (2015), we would expect that adding hyperparameter optimization to recent algorithm selection systems will further boost their performances.
5.6 Handling of Quality Scenarios
ASlib distinguishes between two types of scenarios: runtime scenarios and quality scenarios. In runtime scenarios, the goal is to minimize the time the selected algorithm requires to solve an instances (e.g., SAT, ASP), whereas in quality scenarios the goal is to find the algorithm that obtains the highest score or lowest error according to some metric (e.g., plan quality in AI planning or prediction error in Machine Learning). In the current version of ASlib, the most important difference between the two scenario types is that for runtime scenario a schedule of different algorithms can be provided, whereas for quality scenarios only a single algorithm. The reason for this limitation is that ASlib does not contain information on intermediate solution qualities of any-time algorithms (e.g., the solution quality after half the timeout). For the same reason, the cost of feature computation cannot be considered for quality scenarios – it is unknown how much additional quality could be achieved in the time required for feature computation. This setup is common in algorithm selection methods for machine learning (meta-learning). Intermediate solutions and the time at which they were obtained could enable schedules for quality scenarios and analyzing trade-offs between obtaining a better solution quality by expending more resources or switching to another algorithm. For example, the MiniZinc Challenge (Stuckey et al., 2014) started to record these information in 2017. Future versions of ASlib will consider addressing this limitation.
5.7 Challenging Scenarios
|Scenario||Median rem. gap||Best rem. gap|
On average, algorithm selection systems perform well and the best systems had a remaining gap between the single best and virtual best solver of only in 2017. However, some of the scenarios are harder than others for algorithm selection. Table 5 shows the median and best performance of all submissions on all scenarios. To identify challenging scenarios, we studied the best-performing submission on each scenario and compared the remaining gap with the average remaining gap over all scenarios. In 2015, SAT12-RAND and SAT11-INDU were particularly challenging, and in 2017 OPENML-WEKA-2017 and SAT03-16_INDU.
was a challenging scenario in 2015 and most of the participating systems performed not better than the single best solver on it, although the VBS has a 12-fold speedup over the single best solver. The main reason is probably that not only the SAT instances considered in this scenario are randomly generated but also most of the best-performing solvers are stochastic local search solvers which are highly randomized. The data in this scenario was obtained from single runs of each algorithm, which introduces strong noise. After the competition in 2015, Cameron et al. (2016) showed that in such noisy scenarios, the performance of the virtual best solver is often overestimated. Thus, we do not recommend to study algorithm selection on SAT12-RAND
at this moment and plan to removeSAT12-RAND in the next ASlib release.
was a hard scenario in 2015; in particular it was hard for systems that selected schedules per instance (such as Sunny). Applying schedules on these industrial-like instances is quite hard because even the single best solver has an average PAR10 score of (with a timeout of seconds) to solve an instance; thus, allocating a fraction of the total available resources to an algorithm on this scenario is often not a good idea (also shown by Hoos et al. (2015)).
was a challenging scenario for the participants in . It is mainly an extension of a previously-used scenario called SAT12-INDU. Zilla was one of the best submissions in 2015 on SAT12-INDU with a remaining gap of roughly ; however in 2017 on SAT03-16_INDU, zilla had a remaining gap of . Similar observations apply to ASAP. SAT03-16_INDU could be much harder than SAT12-INDU because of the smaller number of algorithms (), the larger number of instances () or the larger number of instance features ().
was a new scenario in the 2017 competition and appeared to be very challenging, as six out of eight submissions performed almost equal to or worse than the single best solver ( remaining gap). This scenario featured algorithm selection for machine learning problems (cf. meta-learning (Brazdil et al., 2008)). The objective was to select the best machine learning algorithm from a selection of WEKA algorithms (Hall et al., 2009), based on simple characteristics of the dataset (i.e., meta-features). The scenario was introduced by van Rijn (2016)[Chapter 6]. We verified empirically that (i) there is a learnable concept in this scenario, and (ii) the chosen holdout set was sufficiently similar to the training data by evaluating a simple baseline algorithm selector (a regression approach using a random forest as model). The experimental setup and results are presented in Figure 7. It is indeed a challenging scenario; on half of the sampled holdout sets, our baseline was unable to close the gap by more than . In of the holdout sets, the baseline performed worse than the SBS. However, our simple baseline achieved remaining gap on the holdout set used in the competition (compared to the best submission Sunny-fkvar with ).
In this paper, we discussed the setup and results of two algorithm selection competitions. These competitions allow the community to objectively compare different systems and assess their benefits. They confirmed that per-instance algorithm selection can substantially improve the state of the art in many areas of AI. For example, the virtual best solver obtains on average a 31.8 fold speedup over the single best solver on the runtime scenarios from 2017. While the submissions fell short of this perfect performance, they did achieve significant improvements.
Perhaps more importantly, the competitions highlighted challenges for the community in a field that has been well-established for more than a decade. We identified several challenging scenarios on which the recent algorithm selection systems do not perform well. Furthermore, there is no system that performs well on all types of scenarios – a meta-algorithm selection problem is very much relevant in practice and warrants further research. The competitions also highlighted restrictions in the current version of ASlib, which enabled the competitions, that need to be addressed in future work.
Marius Lindauer acknowledges funding by the DFG (German Research Foundation) under Emmy Noether grant HU 1900/2-1.
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Appendix A Submitted Systems in 2015
AutoFolio combines several algorithm selection approaches in a single systems and uses algorithm configuration (Hutter et al., 2011) to search for the best approach and its hyperparameter settings for the scenario at hand.
Sunny selects an algorithm schedule on a per-instance base (Amadini et al., 2014). The time assigned to each algorithm is proportional to the number of solved instances in the neighborhood in the feature space with respect to the instance at hand.
ZillaFolio is a combination of Zilla and AutoFolio by evaluating both approaches on the training set and using the better approach for generating the predictions for the test set.
Appendix B Technical Evaluation Details in 2015
The evaluation was performed as follows. For each scenario, 10 bootstrap samples of the entire data were used to create 10 different train/test splits. No stratification was used. The training part was left unmodified. For the test part, algorithm performances were set to 0 and runstatus to “ok” for all algorithms and all instances – the ASlib specification requires algorithm performance data to be part of a scenario.
There was a time limit of 12 hours for the training phase. Systems that exceeded this limit were disqualified. The time limit was chosen for practical reasons, to make it possible to evaluate the submissions with reasonable resource requirements.
For systems that specified a pre-solver, the instances that were solved by the pre-solver within the specified time were removed from the training set. If a subset of features was specified, only these features (and only the costs associated with these features) were left in both training and test set, with all other feature values removed.
Each system was trained on each train scenario and predicted on each test scenario. In total, 130 evaluations (10 for each of the 13 scenarios) per submitted system were performed. The total CPU time spent was 4685.11 hours on 8-core Xeon E5-2640 CPUs.
Each system was evaluated in terms of mean PAR10 score, mean misclassification penalty (the additional time that was required to solve an instance because an algorithm that was not the best was chosen; the difference to the VBS), and mean number of instances solved for each of the 130 evaluations on each scenario and split. These are the same performance measures used in ASlib, and enable a direct comparison.
The final score of a submission group (i.e. a system submitted for different ASlib scenarios) was computed as the average score over all ASlib scenarios. For scenarios for which no system belonging to the group was submitted, the performance of the single best algorithm was assumed.
Appendix C Submitted Systems in 2017
Gonard et al. (2017) submitted ASAP.v2 and ASAP.v3 (Gonard et al., 2016). ASAP combines pre-solving schedules and per-instance algorithm selection by training both jointly. The main difference between ASAP.v2 and ASAP.v3 is that ASAP.v2 used a pre-solving schedule with a fixed length of 3, whereas ASAP.v3 optimized the schedule length between 1 and 4 on a per-scenario base.
Malone et al. (2017) submitted AS-RF and AS-ASL (Malone et al., 2018). It also combines pre-solving schedules and per-instance algorithm selection, whereas the selection model is a two-level stacking model with the first level being regression models to predict the performance of each algorithm and the second level combines these performance predictions in a multi-class model to obtain a selected algorithm. AS-RF uses random forest and AS-ASL used auto-sklearn (Feurer et al., 2015a) to obtain a machine learning model.
Liu et al. (2017) submitted Sunny (autok and fkvar) (Amadini et al., 2014). Sunny selects per-instance algorithm schedules with the goal of minimizing the number of possible timeouts. Sunny-autok optimized the neighborhood size on a per-scenario base (Lindauer et al., 2016) and Sunny.fkvar additionally also applied greedy forward selection for instance feature subset selection.
Cameron et al. (2017) submitted *Zilla (vanilla and dynamic), the successor of SATzilla (Xu et al., 2008, 2011). *Zilla also combines per-solving schedules and pre-instance algorithm selection but based on pair-wise weighted random forest models. The dynamic version of *Zilla additionally uses the trained random forest to extract a per-instance algorithm schedule.666*Zilla had a critical bug and the results were strongly degraded because of it. The authors of *Zilla submitted fixed results after the official deadline but before the test data and the results were announced. We list the fixed results of *Zilla; but these are not officially part of the competition.
Appendix D Detailed Results 2015 competition
Appendix E Detailed Results 2017 competition