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\documentclass[acmcsur]{acmsmall}
\usepackage{lscape}
\usepackage{longtable}
\usepackage{booktabs}
\title{Algorithm Selection for Combinatorial Search Problems: A survey}
\author{Lars Kotthoff}
\begin{document}
\appendix
\hbadness=10000
\begin{landscape}
\setlength\LTleft{0pt}\setlength\LTright{0pt}
\begin{longtable}{p{6.3em}p{6.5em}p{6em}p{8em}p{10em}p{6em}p{4.5em}}
\toprule
citation & domain & features & predict what & predict how & predict when & portfolio\\\midrule\endhead
\bottomrule\\\multicolumn{7}{r}{continued on next page}\\\endfoot
\bottomrule\\\caption{Summary of the Algorithm Selection literature.}\label{tab:overview}\endlastfoot
\citeA{langley_learningd_1983,langley_learning_1983} & search & past performance
& algorithm & hand-crafted and learned rules & offline and online & dynamic\\
\citeA{carbonell_prodigy_1991} & planning & problem domain features, search
statistics & control rules & explanation-based rule construction & online &
dynamic\\
\citeA{gratch_composer_1992} & planning & problem domain features, search
statistics & control rules & probabilistic rule construction & online &
dynamic\\
\citeA{smith_knowledge-based_1992} & software design & features of abstract
representation & algorithms and data structures & simulated annealing & offline
& static\\
\citeA{aha_generalizing_1992} & machine learning & instance features & algorithm &
learned rules & offline & static\\
\citeA{brodley_automatic_1993} & machine learning & instance and algorithm
features & algorithm & hand-crafted rules & offline & static\\
\citeA{kamel_odexpert_1993} & differential equations & past performance,
instance features & algorithm & hand-crafted rules & offline & static\\
\citeA{minton_integrating_1993,minton_analytic_1993,minton_automatically_1996} &
CSP & runtime performance & algorithm & hand-crafted and learned rules &
offline & dynamic\\
\citeA{cahill_knowledge-based_1994} & software design & instance features &
algorithms and data structures & frame-based knowledge base & offline & static\\
\citeA{tsang_attempt_1995} & CSP & instance features & - & - & - &
static\\
\citeA{brewer_high-level_1995} & software design & runtime performance &
algorithms, data structures and their parameters & statistical model & offline &
static\\
\citeA{weerawarana_pythia_1996,joshi_neuro-fuzzy_1996} & differential equations &
instance features & runtime performance & Bayesian belief propagation, neural
nets & offline & static\\
\citeA{borrett_adaptive_1996} & CSP & search statistics & switch
algorithm? & hand-crafted rules & online & static, static order\\
\citeA{allen_selecting_1996} & SAT, CSP & probing & runtime
performance & hand-crafted rules & online & static\\
\citeA{sakkout_instance_1996} & CSP & search statistics & switch
algorithm? & hand-crafted rules & online & static\\
\citeA{huberman_economics_1997} & graph colouring & past performance & resource
allocation & statistical model & offline & static\\
\citeA{gomes_practical_1997,gomes_algorithm_1997} & CSP & problem size
and past performance & algorithm & statistical model & offline & static\\
\citeA{cook_maximizing_1997} & parallel search & probing & set of search
strategies & decision trees, Bayesian classifier, nearest neighbour, neural
net & online & static\\
\citeA{fink_statistical_1997,fink_how_1998} & planning & past performance &
resource allocation & statistical model, regression & offline & static\\
\citeA{lobjois_branch_1998} & branch and bound & probing & runtime performance &
hand-crafted rules & online & static\\
\citeA{caseau_meta-heuristic_1999} & vehicle routing problem & runtime
performance & algorithm & genetic algorithms & offline & static\\
\citeA{howe_exploiting_1999} & planning & instance features & resource allocation
& linear regression & offline & static\\
\citeA{terashima-marin_evolution_1999} & scheduling & instance and search
features & algorithm & genetic algorithms & offline & dynamic\\
\citeA{wilson_case-based_2000} & software design & instance features & data
structures & nearest neighbour & offline & static\\
\citeA{beck_dynamic_2000} & job shop scheduling & instance feature changes
during search & algorithm scheduling policy & hand-crafted rules & online &
static\\
\citeA{brazdil_comparison_2000} & classification & past performance & ranking &
distribution model & offline & static\\
\citeA{lagoudakis_algorithm_2000} & order selection, sorting & instance
features & remaining cost for each sub-problem & MDP & online & static\\
\citeA{sillito_improvements_2000} & CSP & probing & cost of solving
problem & statistical model & offline & static\\
\citeA{pfahringer_meta-learning_2000} & classification & instance features,
probing & algorithm & 9 different classifiers & offline & static\\
\citeA{fukunaga_genetic_2000} & TSP & past
performance & resource allocation & performance simulation for different
allocations & offline & static\\
\citeA{soares_zoomed_2000} & machine learning & instance features & ranking &
nearest neighbour & offline & static\\
\citeA{gomes_algorithm_2001} & CSP, mixed integer programming &
past performance & algorithm & statistical model & offline & dynamic\\
\citeA{epstein_collaborative_2001,epstein_adaptive_2002,epstein_learning_2005,epstein_learning_2011} &
CSP & variable characteristics & algorithm & weights, hand-crafted rules
& offline and online & dynamic\\
\citeA{lagoudakis_learning_2001} & DPLL branching rules & instance features &
remaining cost for each sub-problem & MDP & online & static\\
\citeA{nareyek_choosing_2001} & optimization & search statistics & expected
utility of algorithm & reinforcement learning & offline and online & static\\
\citeA{horvitz_bayesian_2001} & CSP & instance and instance generator
features, search statistics & runtime performance, restart parameters &
Bayesian model & offline and online & static\\
\citeA{borrett_context_2001} & CSP & instance features, search
statistics & redundant CSP to add & hand-crafted rules & offline & -\\
\citeA{cowling_hyperheuristic_2001,cowling_parameter-free_2001} & scheduling &
past performance & algorithm & reinforcement learning & online & static\\
\citeA{little_capturing_2002} & logic puzzles & instance graph features &
instance model transformations for runtime performance & nearest neighbour &
offline &
-\\
\citeA{petrovic_case-based_2002} & scheduling & instance features & algorithm &
case-based reasoning & offline & static\\
\citeA{leyton-brown_learning_2002} & winner determination problem & instance
features & instance hardness & several forms of regression & offline & static\\
\citeA{fukunaga_automated_2002,fukunaga_automated_2008} & SAT & variable
characteristics & algorithm & genetic algorithms & offline & dynamic\\
\citeA{yu_parallel_2002,yu_adaptive_2004,yu_adaptive_2006} & parallel reduction
algorithms & instance features & algorithm & decision trees, general
linear regression & offline and online & static\\
\citeA{ruan_restart_2002} & SAT & instance features & restart policy & dynamic
programming & offline & static\\
\citeA{burke_tabu-search_2003} & scheduling & past performance & algorithm &
reinforcement learning & online & static\\
\citeA{vrakas_learning_2003} & planning & instance features & parameters &
classification association rules & offline & dynamic\\
\citeA{guo_algorithm_2003} & sorting, probabilistic inference & instance
features & algorithm & decision tree, na\"ive Bayes, Bayesian network,
meta-learning & offline & static\\
\citeA{watson_empirical_2003} & job shop scheduling & instance features, search
statistics & local search algorithm & statistical model & offline and online &
static\\
\citeA{brazdil_ranking_2003} & machine learning & instance features & ranking &
nearest neighbour & offline & static\\
\citeA{gebruers_making_2004} & bid evaluation problem & instance and instance
graph features & solution method & nearest neighbour & offline & static\\
\citeA{guerri_learning_2004} & bid evaluation problem & instance and instance
graph features & solution method, algorithm & decision trees & offline &
static\\
\citeA{beck_simple_2004} & scheduling & probing & algorithm & hand-crafted
rules & offline & static\\
\citeA{nudelman_understanding_2004,xu_satzilla-07_2007,xu_satzilla_2008} & SAT &
instance features, probing & runtime performance & ridge regression, lasso
regression, SVMs, Gaussian processes & offline & static\\
\citeA{carchrae_low-knowledge_2004,carchrae_applying_2005} & job shop scheduling
& probing, search statistics & length of exploration phase, switch
algorithm? & Bayesian classifier, reinforcement learning & offline and online &
static\\
\citeA{soares_meta-learning_2004} & machine learning & instance features &
ranking of SVM kernel widths & nearest neighbour & offline & static\\
\citeA{guo_learning-based_2004} & most probable explanation problem & instance
features & algorithm & decision trees, na\"ive Bayes rules, Bayes networks,
meta-learning techniques & offline & static\\
\citeA{gagliolo_adaptive_2004} & search problems & past performance & resource
allocation & linear model & online & static\\
\citeA{prudencio_meta-learning_2004} & machine learning & instance features &
ranking & decision trees and neural networks & offline & static\\
\citeA{demmel_self-adapting_2005} & linear algebra & instance features &
algorithm & multivariate Bayesian decision rule & offline & static\\
\citeA{gebruers_using_2005} & CSP & instance features & problem model,
solution strategy & nearest neighbour, decision trees, statistical model &
offline & static\\
\citeA{petrik_statistically_2005} & SAT & past performance & resource allocation
& analytic model, MDP & offline and online & static\\
\citeA{cicirello_max_2005} & scheduling & past performance & algorithm &
reinforcement learning & online & static\\
\citeA{gagliolo_neural_2005} & - & past performance & resource allocation &
neural nets & online & static\\
\citeA{gendreau_metaheuristics_2005} & vehicle routing, scheduling & past
performance & algorithm & various & online & static\\
\citeA{armstrong_dynamic_2006} & procedure calls & runtime performance & switch
algorithm? & reinforcement learning & online & static\\
\citeA{gagliolo_learning_2006} & SAT, auction winner determination problem &
past performance & resource allocation & reinforcement learning & online &
static\\
\citeA{roberts_directing_2006} & planning & instance features & resource
allocation & decision trees & offline & static\\
\citeA{hough_modern_2006} & optimization & instance, algorithm and environment
features & algorithm & ensembles of decision trees, SVMs & offline &
static\\
\citeA{bhowmick_application_2006} & linear systems & instance features &
algorithm & boosting, alternating decision trees & offline & static\\
\citeA{hutter_performance_2006} & stochastic local search & instance
features & runtime performance & ridge regression & offline & dynamic\\
\citeA{sayag_combining_2006} & SAT & past performance & resource allocation &
static model, probabilistic model & offline & static\\
\citeA{ali_learning_2006} & classification & instance features & algorithm &
decision rules & offline & static\\
\citeA{cavazos_method-specific_2006} & software design & instance features &
algorithm & logistic regression & offline & static\\
\citeA{burke_case-based_2006} & scheduling & instance features & algorithm &
nearest neighbour & offline & static\\
\citeA{xu_hierarchical_2007} & SAT & instance features & satisfiability and
runtime performance & sparse multinomial logistic regression, ridge
regression & offline & static\\
\citeA{pulina_multi-engine_2007,pulina_self-adaptive_2009,pulina_aqme10_2010} & QBF & instance
features & resource allocation & decision trees, decision rules, logistic
regression, nearest neighbour & offline and online & static\\
\citeA{samulowitz_learning_2007} & QBF & instance features & algorithm,
confidence values & multinomial logistic regression & offline and online &
static\\
\citeA{wu_portfolios_2007} & scheduling & - & portfolio & case-based reasoning &
offline & dynamic\\
\citeA{streeter_combining_2007} & planning & past performance & resource
allocation & statistical model & offline and online & static\\
\citeA{wang_optimizing_2007} & simulation algorithms & past performance & control
parameter & reinforcement learning & online & static\\
\citeA{roberts_learned_2007,roberts_what_2008} & planning & instance features &
runtime, probability of success & 32 different algorithms & offline &
static\\
\citeA{de_la_rosa_case-based_2007,de_la_rosa_using_2007,de_la_rosa_case-based_2013}
& planning & instance features & algorithm & case-based reasoning & online &
static\\
\citeA{steer_information_2008} & - & fitness landscape features & algorithm & -
& offline & static\\
\citeA{streeter_new_2008} & SAT, integer programming, planning & instance features
& resource allocation & statistical model & offline and online & static\\
\citeA{omahony_using_2008,bridge_case-based_2011} & CSP & instance
features, probing & resource allocation & nearest neighbour & offline & static\\
\citeA{kuefler_using_2008} & linear systems & instance features, search
statistics & algorithm & reinforcement learning & online & static\\
\citeA{wei_switching_2008} & SAT & search statistics & algorithm & hand-crafted
rules & online & static\\
\citeA{gagliolo_towards_2008} & SAT & past performance & resource allocation &
reinforcement learning & online & static\\
\citeA{smith-miles_towards_2008} & Quadratic Assignment Problem & instance
features, probing & algorithm, runtime performance & neural networks
and self-organising maps & offline & static\\
\citeA{stergiou_heuristics_2008,stergiou_heuristics_2009,paparrizou_evaluating_2012} & CSP & search statistics & propagation method & clustering & online & static\\
\citeA{de_la_rosa_learning_2008,de_la_rosa_scaling_2011} & planning & instance
features & algorithm & decision tree & online & static\\
\citeA{bai_heuristic_2008} & resource allocation & past performance &
combination of low-level heuristics & various & online & static\\
\citeA{nikoli_instance-based_2009} & SAT & instance features & search strategy &
nearest neighbour & offline & static\\
\citeA{stamatatos_learning_2009} & CSP & probing & propagation method &
clustering & offline & static\\
\citeA{arbelaez_online_2009,arbelaez_continuous_2010} & CSP & instance features, search
statistics & search strategy & SVM & online & static\\
\citeA{haim_restart_2009} & SAT & instance features & restart strategy and
satisfiability & ridge regression, logistic regression & offline & static\\
\citeA{bhowmick_towards_2009} & linear systems & instance features & algorithm &
nearest-neighbour, alternating decision trees, na\"ive Bayes, SVM & offline &
static\\
\citeA{gerevini_automatically_2009} & planning & past performance & macro
actions, resource allocation & performance simulations for different
allocations & offline & static\\
\citeA{xu_learning_2009} & CSP & instance features & algorithm &
reinforcement learning & online & static\\
\citeA{bougeret_combining_2009} & SAT & past performance & resource allocation &
static model & offline & static\\
\citeA{smith-miles_knowledge_2009} & scheduling & instance features & algorithm
& decision tree, neural networks, self-organizing maps & offline & static\\
\citeA{leite_using_2010} & machine learning & past performance, probing &
ranking of classification algorithms & statistical model & offline and online &
static\\
\citeA{silverthorn_latent_2010} & SAT & past performance & runtime performance &
latent class models & offline & static\\
\citeA{stern_collaborative_2010} & QBF, combinatorial auctions & instance and
algorithm features & algorithm & Bayesian model & offline and online &
static\\
\citeA{garrido_dvrp_2010} & dynamic vehicle routing problem & runtime
performance & combination of low-level heuristics & genetic algorithms & online
& dynamic\\
\citeA{domshlak_max_2010} & planning & state variables & algorithm & na\"ive
Bayes classifier & online & static\\
\citeA{kadioglu_isac_2010} & SAT, mixed integer programming, set covering &
instance features & algorithm & clustering & offline & dynamic\\
\citeA{gent_learning_2010} & CSP & instance features, probing & algorithm
& decision trees & offline & static\\
\citeA{gent_machine_2010} & software design & instance features & implementation
& 19 different classifiers & offline & static\\
\citeA{kotthoff_ensemble_2010} & CSP & instance features, probing &
algorithm & ensembles of classifiers & offline & static\\
\citeA{ewald_selecting_2010} & simulation algorithms & past performance &
portfolio & genetic algorithms & offline & dynamic\\
\citeA{elsayed_synthesis_2010,elsayed_synthesis_2011} & CSP & instance
features & search strategy & hand-crafted rules & online & dynamic\\
\citeA{valenzano_simultaneously_2010} & search problems & - & algorithm &
round-robin & online & static\\
\citeA{leite_active_2010} & classification & past performance & ranking &
statistical model & offline & static\\
\citeA{aiguzhinov_similarity-based_2010} & classification & past performance &
ranking & na\"ive Bayes & offline & static\\
\citeA{kanda_using_2010,kanda_selection_2011} & TSP & instance features &
algorithms & nearest neighbour, decision tree, SVM, na\"ive Bayes & offline &
static\\
\citeA{peng_population-based_2010} & numerical optimization & past performance &
resource allocation & optimization & offline & static\\
\citeA{graff_practical_2010} & program induction & fitness function &
runtime performance & regression & offline & static\\
\citeA{fialho_toward_2010} & genetic algorithms & past performance & algorithm &
aggregation & online & static\\
\citeA{burke_genetic_2010} & bin packing & past performance & combinations of
low-level heuristics & genetic programming & online & static\\
% 2011
\citeA{tolpin_rational_2011} & CSP & search statistics & algorithm &
hand-crafted rules & online & static\\
\citeA{malitsky_non-model-based_2011} & SAT & instance features & algorithm &
nearest neighbour & offline & static\\
\citeA{kadioglu_algorithm_2011} & SAT & instance features & resource allocation &
nearest neighbour & offline & static\\
\citeA{kroer_feature_2011} & SAT, CSP & instance features & algorithm &
clustering & offline & dynamic\\
\citeA{kotthoff_preliminary_2011,kotthoff_evaluation_2012} & SAT, QBF,
CSP & instance features, probing & algorithm, runtime performance,
ranking & 31 different machine learning algorithms & offline & static\\
\citeA{gagliolo_algorithm_2010,gagliolo_algorithm_2011} & SAT, QBF, CSP
& past performance & resource allocation & reinforcement learning & online &
static\\
\citeA{gebser_portfolio_2011} & Answer Set Programming & instance features,
probing & runtime performance & SVM & offline & static\\
\citeA{xu_hydra-mip_2011} & MIP & instance features, probing & algorithm &
random forests & offline & dynamic\\
\citeA{maturana_adaptive_2011} & evolutionary algorithms & past performance &
algorithm & statistical models & online & static\\
\citeA{helmert_fast_2011} & planning & past performance & resource allocation &
statistical model & offline & static\\
\citeA{kiziltan_classification-based_2011} & CSP & instance features &
resource allocation & 8 classification algorithms, ridge regression & offline &
static\\
\citeA{smith-miles_discovering_2011} & TSP & instance features & algorithm &
self-organizing map, decision tree, neural network & offline & static\\
\citeA{jankowski_selecting_2011} & machine learning & instance features &
ranking & nearest neighbour & offline & static\\
\citeA{hoffman_portfolio_2011} & Bayesian Optimization & past performance &
algorithm & multi-armed bandits & online & static\\
% 2012
\citeA{kotthoff_hybrid_2012} & SAT, QBF, CSP & instance features,
probing & algorithm & 5 regression algorithms, 2 classification
algorithms & offline & static\\
\citeA{yun_learning_2012} & CSP & instance features & portfolio &
case-based reasoning, hand-crafted rules & offline & dynamic\\
\citeA{hurley_adaptation_2012} & SAT & instance features & ranking & case-based
reasoning with voting & offline & static\\
\citeA{shukla_genetic-algorithms-based_2012} & inventory routing problem &
past performance & portfolio & statistical model & offline & static\\
\citeA{malitsky_parallel_2012} & SAT & past performance & resource allocation &
nearest neighbour & offline and online & static\\
\citeA{bischl_algorithm_2012} & optimization & instance features &
algorithm & SVM & offline & static\\
\citeA{veerapen_exploration-exploitation_2012} & Quadratic Assignment Problem
and TSP & past performance & algorithm & statistical model & online & static\\
\citeA{valenzano_arvandherd_2012} & planning & past performance & resource
allocation & statistical model & offline and online & static\\
\citeA{hutter_algorithm_2012,hutter_algorithm_2014} & SAT, MIP, TSP & instance
features & algorithm performance & 11 regression algorithms & offline & static\\
\citeA{kanda_meta-learning_2012,kanda_meta-learning_2016} & TSP & instance
features & ranking & neural networks, nearest neighbour, clustering trees &
offline & static\\
\citeA{kadioglu_non-model-based_2012} & MIP & instance features & algorithm &
clustering & online & static\\
\citeA{seipp_learning_2012} & planning & past performance & resource allocation
& clustering and heuristic approaches & offline & static\\
\citeA{maratea_applying_2012,maratea_multi-engine_2013} & ASP & instance
features & algorithm & classification & offline & static\\
\citeA{munoz_meta-learning_2012} & optimization & instance features, algorithm
parameters & runtime performance & neural network regression & offline &
static\\
\citeA{park_using_2012} & software design & instance features & runtime
performance & SVM & offline & static\\
\citeA{morak_evaluating_2012} & ASP & instance features & algorithm &
classification and regression & offline & static\\
\citeA{burke_monte_2012} & scheduling & past performance & algorithm &
reinforcement learning & offline & static\\
\citeA{pillay_study_2012} & bin packing & past performance & combination of
low-level heuristics & genetic algorithm & offline & static\\
\citeA{hu_intelligent_2012} & evolutionary algorithms & past performance &
algorithm & hand-crafted rule & online & static\\
% 2013
\citeA{sabharwal_boosting_2013} & SAT & instance features & resource allocation
and switch algorithm? & nearest neighbour and decision tree classification &
offline and online & static\\
\citeA{abell_features_2013} & black-box optimization & instance features &
algorithm & clustering & offline & static\\
\citeA{hutter_identifying_2013} & SAT, MIP, TSP & instance features and algorithm
parameters & algorithm performance & random forests, linear regression, neural
networks, Gaussian processes, regression trees & offline & static\\
\citeA{musliu_algorithm_2013} & graph colouring & instance features & algorithm &
six classifiers & offline & static\\
\citeA{amadini_empirical_2013} & CSP & instance features & algorithm &
range of different approaches & offline & static\\
\citeA{alhossaini_instance-specific_2013} & planning & instance features &
model & SVM & offline & static\\
\citeA{seijen_efficient_2013} & reinforcement learning & past performance &
abstraction & MDP & online & static\\
\citeA{malitsky_evolving_2013} & SAT & instance features & algorithm & clustering
& online & static\\
\citeA{mehta_lazy_2013} & CSP & instance features & algorithm &
classification, regression and clustering & offline & static\\
\citeA{malitsky_algorithm_2013} & SAT & instance features & algorithm &
classification & offline & static\\
\citeA{rayner_subset_2013} & combinatorial search & probing & subset of
algorithms & optimization & offline & static\\
\citeA{sun_pairwise_2013} & machine learning & past performance & ranking &
pairwise rules and trees & offline & static\\
\citeA{collautti_snnap_2013} & SAT & instance features, past performance &
algorithm & nearest neighbour, random forests & offline & static\\
\citeA{maratea_automated_2013} & ASP & instance features & algorithm & PART
decision rules & offline & static\\
\citeA{wang_feature_2013} & feature selection & instance features & algorithm &
nearest neighbour and optimization & offline & static\\
\citeA{king_autonomic_2013,king_network_2014} & power systems & instance
features & algorithm & neural net, decision tree, random forest & offline &
static\\
\citeA{yuen_which_2013} & evolutionary algorithms & past performance & algorithm
& linear regression & online & static\\
\citeA{loth_bandit-based_2013} & CSP & past performance &
algorithm & reinforcement learning & online & static\\
\citeA{simon_automatic_2013} & software design & instance features & algorithm &
neural networks, decision trees & offline & dynamic\\
\citeA{geschwender_selecting_2013,geschwender_portfolio_2016} & CSP &
instance features & algorithm & decision tree, neural network, naive Bayes & offline & static\\
\citeA{nikolic_simple_2013} & SAT & instance features & algorithm & nearest
neighbour & offline & static\\
\citeA{kendall_competitive_2013} & competitive TSP & instance features &
algorithm & Bayesian approach & online & static\\
% 2014
\citeA{amadini_portfolio_2014} & CSP & instance features & algorithm,
resource allocation & 5 different classifiers & offline and online & static\\
\citeA{cauwet_algorithm_2014} & optimization & past performance & resource
allocation & statistical model & online & static\\
\citeA{hoos_aspeed_2014} & ASP, SAT, QBF, CSP & past performance & resource
allocation & answer set programming & offline & static\\
\citeA{hurley_proteus_2014} & CSP & instance features & instance representation,
algorithm & classification, regression, clustering & offline & static\\
\citeA{kotthoff_ranking_2014} & CSP, SAT, QBF & instance features & ranking &
classification, regression, meta-learning & offline & static\\
\citeA{tang_population-based_2014} & numerical optimization & past performance &
algorithm portfolio & optimization & offline & dynamic\\
\citeA{fawcett_improved_2014} & planning & instance features & runtime &
regression & offline & static\\
\citeA{amadini_sequential_2014,amadini_sunny_2014,amadini_sunny-cp_2015,amadini_multicore_2015} &
CSP & instance features & resource allocation
& nearest neighbour & offline & static\\
\citeA{blet_experimental_2014} & CSP & instance features & algorithm & M5P
regression & offline & static\\
\citeA{malitsky_portfolio_2014} & Minimal Correction Subset & instance features,
past performance & algorithm & nearest neighbour, random forests & offline &
static\\
\citeA{malitsky_timeout-sensitive_2014} & Minimal Correction Subset & instance
features & resource allocation & nearest neighbour, regression & offline &
static\\
\citeA{ansotegui_maxsat_2014} & MaxSAT & instance features & algorithm &
clustering & offline & static\\
\citeA{malitsky_latent_2014} & CSP, MaxSAT, SAT & instance features, past
performance & algorithm & random forest and linear regression & offline &
static\\
\citeA{smith_recommending_2014} & classification & past performance & algorithm
& collaborative filtering & offline & static\\
\citeA{garbajosa_planning_2014} & planning & instance features & algorithm &
classifier ensemble & online & static\\
\citeA{pihera_application_2014} & TSP & instance features & algorithm & 5
classifiers & offline & static\\
\citeA{st-pierre_nash_2014} & Go & past performance & policy & static rule and
reinforcement learning & offline and online & static\\
\citeA{van_rijn_algorithm_2014} & machine learning & instance features &
algorithm & decision stumps, random forests & offline & static\\
\citeA{lieder_algorithm_2014} & sorting & instance features & performance &
Bayesian regression & offline & static\\
\citeA{lindauer_algorithm_2014} & ASP, CSP, SAT, QBF, OR & instance features &
resource allocation & lots & offline & static\\
\citeA{hoos_claspfolio_2014} & ASP & instance features & resource allocation &
pairwise classification, regression, clustering & offline & static\\
\citeA{sukhija_portfolio-based_2014} & loop scheduling & instance features &
algorithm & classification & offline & static\\
\citeA{stojadinovic_instance-based_2014} & CSP & instance features & algorithm &
nearest neighbour & offline & static\\
\citeA{shahriari_entropy_2014} & Bayesian Optimization & entropy & algorithm &
multi-armed bandits & online & static\\
\citeA{lopez-camacho_unified_2014} & bin packing & instance features & algorithm
& nearest neighbour & online & static\\
\citeA{salcedo-sanz_evolutionary-based_2014} & games & past performance &
combination of low-level heuristics & genetic algorithm & offline & static\\
\citeA{sagarna_assisting_2014} & software testing & instance features &
algorithm & Bayesian network & offline & static\\
% 2015
\citeA{tierney_algorithm_2015} & container premarshalling & instance features,
past performance & algorithm & hierarchical cost-sensitive clustering & offline
& static\\
\citeA{lindauer_sequential_2015} & SAT, QBF, ASP, container premarshalling &
instance features & resource allocation & random forest pairwise classification,
ridge regression, k-means clustering & offline & static\\
\citeA{lindauer_autofolio_2015,lindauer_autofolio_2015-1} & ASlib & instance
features & resource allocation & pairwise classification, regression, clustering
& offline & static\\
\citeA{kotthoff_improving_2015} & TSP & instance features & algorithm &
classification, regression, pairwise regression & offline & static\\
\citeA{sabar_population_2015} & combinatorial search & past performance &
algorithm & reinforcement learning & online & static\\
\citeA{oentaryo_algorithm_2015} & SAT & instance features and past performance &
ranking & stochastic optimization & offline & static\\
\citeA{chu_learning_2015} & CSP & instance features & algorithm &
partial least squares regression & offline & static\\
\citeA{balafrej_multi-armed_2015} & CSP & past performance & propagation
method & multi-armed bandits & online & static\\
\citeA{luo_fast_2015} & stencil computation & instance features & solution space
& multiple linear regression & offline & static\\
\citeA{ilany_algorithm_2015} & multi-agent systems & instance features & runtime
performance & linear regression, regression trees, neural network, multi-armed
bandits & offline and online & static\\
\citeA{everitt_analytical_2015,everitt_analytical_2015-1} & search & instance
features & runtime performance & analytical model & offline & static\\
\citeA{amadini_sunny_2015} & ASlib & instance features & resource allocation &
nearest neighbour & offline & static\\
\citeA{phillips_efficient_2015} & search & past performance & resource
allocation & multi-armed bandits & online & static\\
\citeA{abseher_improving_2015} & tree decomposition & instance features &
ranking & linear regression, nearest neighbour, regression trees, neural
network, SVM & offline & static\\
\citeA{yuen_sequential_2015,lou_sequential_2017,yuen_selecting_2018} & black-box
optimization & instance features & algorithm & nearest neighbour & offline &
static\\
% 2016
\citeA{palmieri_parallel_2016} & constraint programming & past performance &
algorithm & statistical test & online & static\\
\citeA{inala_synthesis_2016} & SMT & past performance & encoding & pattern
matching & offline & dynamic\\
\citeA{mendes_hyper-heuristic_2016} & games & instance features & algorithm &
various classifiers & offline & static\\
\citeA{bontrager_matching_2016} & games & instance features & algorithm &
hierarchical clustering and decision trees & offline & static\\
\citeA{koitz_improving_2016,koitz_exploiting_2016,koitz-hristov_applying_2018} &
abductive diagnosis & instance features & algorithm & various classifiers &
offline & static\\
\citeA{minot_using_2016} & sum coloring problem & instance features & algorithm &
hand-crafted rule & offline & static\\
\citeA{kotthoff_portfolios_2016} & subgraph isomorphism & instance features &
algorithm & classification, regression, pairwise classification and regression
& offline & static\\
\citeA{degroote_reinforcement_2016,degroote_regression-based_2018} & ASlib &
instance features & algorithm & random forest regression & offline & static\\
\citeA{gonard_algorithm_2016} & ASlib & instance features & resource allocation
& random forest and nearest neighbour regression & offline & static\\
\citeA{sidnev_hardware-specific_2016} & matrix multiplication, sorting, linear
equations, FFT & instance features & runtime performance, algorithm & linear
regression & offline & static\\
\citeA{benatia_machine_2016,benatia_sparse_2016} & sparse matrix-vector
multiplication & instance features & runtime performance & SVM, neural network &
offline & static\\
\citeA{dutt_plan_2016} & database query processing & instance features & resource
allocation & optimization & offline & static\\
\citeA{liberto_dash_2016} & MIP & instance features, search statistics &
algorithm & clustering & online & static\\
\citeA{lindauer_empirical_2016} & ASlib & instance features & resource
allocation & nearest neighbour & offline & static\\
\citeA{khalil_learning_2016} & MIP & instance features, search statistics &
ranking & SVM & online & static\\
\citeA{cenamor_ibacop_2016} & planning & instance features & resource allocation
& classification, regression & offline & static\\
\citeA{cunha_selecting_2016,cunha_recommending_2017,cunha_metalearning_2018} &
recommender systems & instance features, probing & algorithm & classification &
offline & static\\
\citeA{cui_recommendation_2016,chu_adaptive_2019} & evolutionary algorithms &
instance features & ranking & nearest neighbour, neural network & online &
static\\
\citeA{cui_short-term_2016} & building energy optimization & instance features &
ranking & neural network & offline & static\\
% 2017
\citeA{misir_alors_2017} & ASlib & instance and algorithm features & ranking &
matrix completion & offline & static\\
\citeA{ansotegui_reactive_2017} & MaxSAT & instance features, past performance &
algorithm & search & offline and online & dynamic\\
\citeA{minot_combining_2017} & sum coloring problem & instance features &
algorithm & pairwise random regression forests & offline & static\\
\citeA{zaharija_cognitive_2017} & robotics & instance features & algorithm &
hand-crafted rules & offline & static\\
\citeA{wagner_improving_2017} & minimum vertex cover & instance features &
algorithm & pairwise classification, regression, clustering & offline & static\\
\citeA{chen_instance-specific_2017} & SAT, MaxSAT & instance features &
algorithm & multi-output learning & offline & static\\
\citeA{khalil_learning_2017} & MIP & instance features, search statistics &
algorithm & logistic regression & online & static\\
\citeA{gnad_beyond_2017} & planning & probing & ranking & static rule & offline
& static\\
\citeA{fitzgerald_analysing_2017} & CSP, SAT, combinatorial auctions & past
performance & algorithm & reinforcement learning & online & static\\
\citeA{beham_instance-based_2017,beham_algorithm_2018} & Quadratic Assignment
Problem & instance features, probing & ranking & nearest neighbour & offline & static\\
\citeA{selvaraj_pce-based_2017} & optical network design & instance features &
algorithm & - & offline & static\\
\citeA{cunha_metalearning_2017} & recommender systems & instance features &
ranking & nearest neighbour, naive Bayes, trees & offline & static\\
\citeA{stephenson_creating_2017} & Angry Birds & instance features & ranking &
classification & offline & static\\
\citeA{li_hyperheuristic_2017} & games & past performance & algorithm &
reinforcement learning & online & static\\
\citeA{he_bayesian_2017} & black-box optimization & past performance & algorithm
& Bayesian approach & offline & static\\
%2018
\citeA{fuentetaja_meta-search_2018} & planning & past performance & instance
representation, algorithm & optimization & offline & dynamic\\
\citeA{jana_landscape_2018} & protein structure prediction & instance features &
algorithm & hand-crafted rule & offline & static\\
\citeA{jankee_design_2018} & black-box optimization & past performance &
algorithm & bandit algorithms & offline & static\\
\citeA{georges_feature-based_2018} & MIP & instance features, probing &
portfolio & classification, regression, boosting & offline & static and dynamic\\
\citeA{silva_strategy_2018} & games & instance features & algorithm &
logistic regression & online & dynamic\\
\citeA{degroote_applying_2018} & Generalized Assignment Problem & instance
features & algorithm & random forest & offline & static\\
\citeA{gudu_combinatorial_2018} & combinatorial auctions & instance features &
algorithm & auto-sklearn & offline & static\\
\citeA{elmandouh_guiding_2018} & formal verification & instance features &
resource allocation & classification & offline & static\\
\citeA{ansotegui_self-configuring_2018} & CSP & instance features & resource
allocation & classification & offline & static\\
\citeA{hoos_portfolio-based_2018} & QBF & instance features & algorithm &
autofolio & offline & static\\
\citeA{nikolic_portfolio_2018} & theorem proving & instance features &
algorithm, runtime performance & classification, regression & offline & static\\
\citeA{deng_automatic_2018} & classification & instance features & algorithm &
clustering & offline & static\\
\citeA{wang_wombit_2018} & CSP & instance features & algorithm & decision tree &
offline & static\\
\citeA{tripoul_there_2018} & pattern matching & simulation & constraint & hand-crafted model &
online & static\\
\citeA{pavelski_meta-learning_2018,pavelski_recommending_2018} & flowshop &
instance features & algorithm & decision trees, gradient boosting & offline & static\\
\citeA{alcobaca_dimensionality_2018} & machine learning & instance features &
algorithm & classification & offline & static\\
\citeA{kerschke_leveraging_2018} & TSP & instance features & algorithm &
classification, regression & offline & static\\
% 2019
\citeA{loera_data-driven_2019} & optimization & instance & algorithm &
neural networks & offline & static\\
\citeA{mantovani_meta-learning_2019} & machine learning & instance features &
algorithm & classification & offline & static\\
\citeA{abdulrahman_simplifying_2019} & machine learning & instance features &
algorithm & ranking & offline & static\\
\end{longtable}
\end{landscape}
\hbadness=1000
\bibliographystyle{acmsmall}
\bibliography{survey}
\end{document}