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%%%%%%%%%%%%%%%%%%%-*- mode: bibtex; bibtex-maintain-sorted-entries: plain; bibtex-string-files: ("abbrev.bib" "journals.bib" "authors.bib") -*-
%% biblio.bib : https://iridia-ulb.github.io/references/
%%
%% To the extent that the contents of bib files may be subject to
%% copyright, the contents of the IRIDIA BibTeX Repository are placed
%% under the public domain by associating it to the Creative Commons CC0
%% 1.0 Universal license (http://creativecommons.org/publicdomain/zero/1.0/).
%
%% READ THESE RULES FIRST BEFORE MODIFYING THIS FILE
%% 0. This file should not contain 'article' entries. Put them in articles.bib.
%% 1. Keep the entries sorted with respect to the key.
%% 2. Check that what you are adding has not been added already with a
%% different key.
%% 3. 'author' and 'editor' fields should be taken from authors.bib.
%% 4. 'publishers', 'series' and 'institution' should be taken from abbrev.bib.
%% 5. 'InProceedings', 'InCollection' and 'InBook' should take cross-references
%% from crossref.bib
%% 6. The 'alias' field is used when a repeated entry is found.
%% Delete one and add its key as the alias field of the other.
%% This helps to locate entries that have been renamed or deleted.
%% 7. Papers in proceedings published as books (e.g., LNCS) should use
%% InCollection type.
%% 8. InBook should be used ONLY for books where chapters do not have titles.
%% 9. Since some bib-styles mandate title-case but others mandate sentence-case
%% and converting from title-case to sentence-case is done automatically but
%% the opposite cannot be done, then titles should preferably be in
%% title-case like "Data Structures and Algorithms". If a word or a letter
%% should always be in upper (or lower) case, then surround them with
%% braces, like in "The {ACO} Book", or "The {Pareto} Front".
%%10. Braces can prevent kerning between letters, so it is in general
%% preferable to enclose entire words and not just single letters in braces
%% to protect them.
%%11. The fields 'pdf', 'supplement', and 'epub' can be used to point out to a
%% preferred PDF filename, a url containing supplementary material and a url
%% containing the document, respectively. These fields will be ignored by
%% most BibTeX styles (.bst files), but they can be used with custom styles
%% to, for example, generate an HTML bibliography, a list of publications.
%% See testbib.tex for an example.
%%12. If you wish to add an entry for supplementary material to another
%% publication, please use a Misc entry with the same label as for the main
%% publication adding -supp at the end. Example: the supplementary material
%% entry for BezLopStu2012:ants would be BezLopStu2012:ants-supp.
%% This keeps the entries together, making easier to keep them in sync.
%%13. Use the following abbreviations for months: jan, feb, mar, apr, may, jun,
%% jul, aug, sep, oct, nov, dec. Some bibstyles will abbreviate the months,
%% others may use numeric values and others will use the full name. Using
%% the abbreviations allows this customization to be consistent.
%%14. biblatex warns if 'month' field contains more than one month.
%% Use the 'date' field in that case.
%% Z. Some of the entries do not follow the above rules.
%% Please help us to update them little by little.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
@Misc{AAAI2021checklist,
author = "{AAAI}",
title = "35th AAAI Conference on Artificial Intelligence:
Reproducibility Checklist",
howpublished =
"\url{https://aaai.org/Conferences/AAAI-21/reproducibility-checklist/}",
year = 2021,
note = "Last accessed: June 6th, 2021"
}
@Misc{ACM2020badging_v1_1,
author = {{ACM}},
title = "Artifact Review and Badging Version 1.1",
howpublished =
"\url{https://www.acm.org/publications/policies/artifact-review-and-badging-current}",
year = 2020,
month = aug,
}
@InCollection{AarKorMic2005,
title = {Simulated Annealing},
author = Aarts #and# Korst_JHM #and# Michiels_W,
crossref = "SearchMethod2005",
pages = {187--210},
}
@InProceedings{Abb2002selfpde,
title = {The self-adaptive {Pareto} differential evolution algorithm},
author = Abbass,
crossref = {CEC2002},
pages = {831--836},
}
@InProceedings{LimPoz2017automopso,
author = {de Lima, Ricardo Henrique Remes and Pozo, Aurora Trinidad
Ramirez},
title = {A study on auto-configuration of Multi-Objective Particle
Swarm Optimization Algorithm},
crossref = "CEC2017",
pages = {718--725},
doi = {10.1109/CEC.2017.7969381}
}
@InProceedings{AbbSarNew2001pde,
title = {{PDE}: a {Pareto}-frontier differential evolution approach
for multi-objective optimization problems},
author = Abbass #and# {Sarker, Ruhul and Newton, Charles},
crossref = {CEC2001},
pages = {971--978}
}
@InProceedings{AbdKriCha1997,
title = {A hybrid heuristic for multiobjective knapsack problems},
author = {Ben Abdelaziz, F. and Krichen, S. and Chaouachi, J.},
pages = {205--212},
crossref = "MIC1997",
doi = "10.1007/978-1-4615-5775-3_14"
}
@InCollection{Aca2004memaco,
author = {Acan, A.},
title = {An external memory implementation in ant colony optimization},
crossref = "ANTS2004",
pages = {73--84},
keywords = {memory-based ACO}
}
@InCollection{Aca2005evocop,
author = {Acan, A.},
title = {An external partial permutations memory for ant colony
optimization},
crossref = "EVOCOP2005",
pages = {1--11},
keywords = {memory-based ACO}
}
@InCollection{AguZapLieVer2016many,
title = {Approaches for Many-Objective Optimization: Analysis and
Comparison on {MNK}-Landscapes},
author = Aguirre #and# Zapotecas_S #and# Liefooghe #and# Verel #and#
Tanaka,
crossref = "EA2015",
pages = {14--28},
doi = "10.1007/978-3-319-31471-6_2"
}
@InProceedings{CheHuhHul2009dt,
author = {Cheng, Weiwei and H\"{u}hn, Jens} #and# Huellermeier,
title = {Decision Tree and Instance-Based Learning for Label Ranking},
crossref = "ICML2009",
doi = {10.1145/1553374.1553395},
pages = {161--168},
numpages = 8,
}
@InCollection{AguTan2009:space,
crossref = {EMO2009},
title = {Many-Objective Optimization by Space Partitioning and
Adaptive $\epsilon$-Ranking on {MNK}-Landscapes},
author = Aguirre #and# Tanaka,
pages = {407--422},
}
@InCollection{Aguirre2013,
author = Aguirre,
title = {Advances on Many-objective Evolutionary Optimization},
pages = {641--666},
crossref = "GECCO2013c",
keywords = {many-objective evolutionary optimization}
}
@Book{AhoHopUll83:data-structures,
author = Aho #and# Hopcroft #and# Ullman,
title = {Data structures and algorithms},
publisher = {Addison-Wesley, Reading, MA},
year = 1983
}
@Book{AhujMagOrl1993netflows,
author = Ahuja_RK #and# {T. Magnanti} #and# Orlin_JB,
title = {Network Flows: Theory, Algorithms and Applications},
publisher = {Prentice-Hall},
year = 1993,
}
@InCollection{AikBurLi2006,
author = {Uwe Aickelin} #and# Burke_E #and# {Jingpeng Li},
title = {Improved Squeaky Wheel Optimisation for Driver Scheduling},
crossref = {PPSN2006},
pages = {182--191},
}
@InCollection{AisRoy2010:isorms,
author = Aissi #and# Roy,
title = "Robustness in Multi-criteria Decision Aiding",
crossref = {EhrFigGre2010:isorms},
chapter = 4,
pages = "87--121"
}
@InCollection{AkiSanYan2019optuna,
doi = {10.1145/3292500.3330701},
author = {Takuya Akiba and Shotaro Sano and Toshihiko Yanase and Takeru
Ohta and Masanori Koyama},
title = {Optuna: A Next-generation Hyperparameter Optimization Framework},
pages = "2623--2631",
crossref = "SIGKDD2019"
}
@TechReport{AktAtaGur2007conic,
author = {S. M. Akt{\"u}rk} #and# Atamturk_A #and# {S. G{\"u}rel},
title = {A Strong Conic Quadratic Reformulation for Machine-Job
Assignment with Controllable Processing Times},
institution = {University of California-Berkeley},
year = 2007,
type = {Research Report},
number = {BCOL.07.01}
}
@InCollection{AlaSolGhe07,
author = {I. Alaya} #and# Solnon #and# { Khaled Gh{\'e}dira},
title = {Ant Colony Optimization for Multi-Objective
Optimization Problems},
booktitle = {19th IEEE International Conference on Tools with
Artificial Intelligence (ICTAI 2007)},
year = 2007,
volume = 1,
publisher = ieee-csp,
address = ieee-csp-ad,
pages = {450--457},
}
@InProceedings{AlaSolGhe2004:bioma,
author = {I. Alaya} #and# Solnon #and# { Khaled Gh{\'e}dira},
title = {Ant algorithm for the multi-dimensional knapsack
problem},
pages = {63--72},
crossref = "BIOMA2004",
}
@InCollection{AlbChi2007gecco,
author = Alba_E #and# Chicano_F,
title = {{ACOhg}: dealing with huge graphs},
pages = {10--17},
crossref = {GECCO2007},
doi = {10.1145/1276958.1276961},
}
@InCollection{AliSimHar2019,
author = {Alissa, Mohamad and Sim, Kevin} #and# Hart_E,
title = {Algorithm Selection Using Deep Learning without Feature Extraction},
crossref = "GECCO2019",
pages = {198--206}
}
@InCollection{AllBurHyd2009reusable,
author = {Allen, Sam} #and# Burke_E #and# Hyde_M #and# Kendall_G,
title = {Evolving reusable 3d packing heuristics with genetic
programming},
pages = {931--938},
crossref = {GECCO2009},
doi = {10.1145/1569901.1570029},
keywords = {hyper-heuristic}
}
@InCollection{AllKno2010variables,
author = Allmendinger #and# Knowles,
title = {Evolutionary Optimization on Problems Subject to Changes of
Variables},
editor = {Schaefer, Robert} #and# Cotta #and# {Ko{\l}odziej, Joanna}
#and# Rudolph_G,
pages = {151--160},
crossref = {PPSN2010},
abstract = {Motivated by an experimental problem involving the
identification of effective drug combinations drawn from a
non-static drug library, this paper examines evolutionary
algorithm strategies for dealing with changes of
variables. We consider four standard techniques from dynamic
optimization, and propose one new technique. The results show
that only little additional diversity needs to be introduced
into the population when changing a small number of
variables, while changing many variables or optimizing a
rugged landscape requires often a restart of the optimization
process}
}
@InProceedings{AllKno2011ecta,
author = Allmendinger #and# Knowles,
title = {Evolutionary Search in Lethal Environments},
booktitle = {International Conference on Evolutionary Computation Theory
and Applications},
year = 2011,
pages = {63--72},
publisher = {SciTePress},
doi = {10.5220/0003673000630072},
epub = {https://www.scitepress.org/papers/2011/36730/36730.pdf}
}
@InCollection{AllKno2011policy,
author = Allmendinger #and# Knowles,
title = {Policy Learning in Resource-Constrained Optimization},
pages = {1971--1979},
crossref = {GECCO2011},
doi = {10.1145/2001576.2001841},
abstract = {We consider an optimization scenario in which resources are
required in the evaluation process of candidate
solutions. The challenge we are focussing on is that certain
resources have to be committed to for some period of time
whenever they are used by an optimizer. This has the effect
that certain solutions may be temporarily non-evaluable
during the optimization. Previous analysis revealed that
evolutionary algorithms (EAs) can be effective against this
resourcing issue when augmented with static strategies for
dealing with non-evaluable solutions, such as repairing,
waiting, or penalty methods. Moreover, it is possible to
select a suitable strategy for resource-constrained problems
offline if the resourcing issue is known in advance. In this
paper we demonstrate that an EA that uses a reinforcement
learning (RL) agent, here Sarsa({$\lambda$}), to learn
offline when to switch between static strategies, can be more
effective than any of the static strategies themselves. We
also show that learning the same task as the RL agent but
online using an adaptive strategy selection method, here
D-MAB, is not as effective; nevertheless, online learning is
an alternative to static strategies.},
isbn = {978-1-4503-0557-0},
langid = {english}
}
@InProceedings{AllMouLiu2019human,
author = {Joseph Allen and Ahmed Moussa and Xudong Liu},
title = {Human-in-the-Loop Learning of Qualitative Preference Models},
pages = {108--111},
crossref = {FLAIRS2019},
doi = {10.48550/arXiv.1909.09064}
}
@PhDThesis{Allmendinger2012phd,
author = Allmendinger,
title = {Tuning Evolutionary Search for Closed-Loop Optimization},
school = umanchester,
year = 2012,
month = jan
}
@InProceedings{AlsTsa2009,
title = {Guided {Pareto} local search and its application to
the 0/1 multi-objective knapsack problems},
author = {Alsheddy, A. and Tsang, E.},
crossref = "MIC2009"
}
@InProceedings{AmaAliThr2019nips,
title = {Linear Stochastic Bandits Under Safety Constraints},
author = {Amani, Sanae and Alizadeh, Mahnoosh and Thrampoulidis,
Christos},
pages = {9256--9266},
crossref = "NIPS2019",
epub =
{http://papers.nips.cc/paper/9124-linear-stochastic-bandits-under-safety-constraints.pdf}
}
@InCollection{AndVidIve1993,
title = {Design of a Teleprocessing Communication Network Using Simulated Annealing},
author = Andersen_K #and# Vidal_RVV #and# Iversen_VB,
pages = {201--215},
crossref = {Vidal1993}
}
@InCollection{Andersen99,
author = Andersen_JH #and# Powell_RS,
title = "The Use of Continuous Decision Variables in an
Optimising Fixed Speed Pump Scheduling Algorithm",
booktitle = "Computing and Control for the Water Industry",
pages = {119--128},
publisher = rsp,
year = 1999,
editor = Powell_RS #and# Hindi,
}
@InCollection{AngBocPaoVec08,
title = {Performance Evaluation of an Adaptive Ant Colony
Optimization Applied to Single Machine Scheduling},
author = {D. Anghinolfi and A. Boccalatte and M. Paolucci and
C. Vecchiola},
pages = {411--420},
crossref = "SEAL2008",
}
@InCollection{Angus2007,
author = Angus,
title = {Population-Based Ant Colony Optimisation for
Multi-objective Function Optimisation},
crossref = "ACAL2007",
pages = {232--244},
doi = {10.1007/978-3-540-76931-6_21},
}
@InProceedings{AnsKamVeeRag2014open,
author = {J. Ansel and S. Kamil and K. Veeramachaneni and J. Ragan-Kelley and J. Bosboom} #and# OReilly_UM #and# {S. Amarasinghe},
title = {{OpenTuner}: An extensible framework for program autotuning},
crossref = "PACT2014",
pages = {303--315},
doi = {10.1145/2628071.2628092}
}
@InProceedings{AnsMalSamSelTie2015:ijcai,
author = Ansotegui #and# Malitsky_Y #and# {Horst Samulowitz} #and#
Sellmann #and# Tierney,
title = {Model-Based Genetic Algorithms for Algorithm Configuration},
pages = {733--739},
crossref = {IJCAI2015},
keywords = {GGA++},
epub = {https://www.ijcai.org/Abstract/15/109}
}
@InProceedings{AnsMalSel2014isacpp,
author = Ansotegui #and# Malitsky_Y #and# Sellmann,
title = {{MaxSAT} by Improved Instance-Specific Algorithm
Configuration},
pages = {2594--2600},
crossref = {AAAI2014}
}
@InCollection{AnsSelTie2009cp,
author = Ansotegui #and# Sellmann #and# Tierney,
title = {A Gender-Based Genetic Algorithm for the Automatic
Configuration of Algorithms},
pages = {142--157},
doi = {10.1007/978-3-642-04244-7_14},
crossref = "CP2009",
alias = "Ansotegui2009",
keywords = "GGA"
}
@TechReport{AppBixChvCoo95:tr,
author = Applegate_D #and# Bixby #and# Chvatal #and# Cook_W,
title = "Finding Cuts in the {TSP}",
institution = dimacs,
year = 1995,
number = "95--05",
month = mar,
}
@TechReport{AppBixChvCoo99:tr,
author = Applegate_D #and# Bixby #and# Chvatal #and# Cook_W,
title = {Finding Tours in the {TSP}},
institution = fdm_bonn,
year = 1999,
number = 99885
}
@Book{AppEtAl06,
author = Applegate_D #and# Bixby #and# Chvatal #and# Cook_W,
title = "The Traveling Salesman Problem: A Computational Study",
publisher = pup,
year = 2006
}
@InProceedings{AprGloKel2003,
author = April_Jay #and# Glover_F #and# Kelly_J #and# Laguna,
title = "Simulation-based optimization: Practical introduction to simulation optimization",
pages = {71--78},
crossref = "SIMCONF2003",
doi = {10.1109/WSC.2003.1261410},
}
@Book{AroBar2009,
title = {Computational complexity: a modern approach},
author = {Arora, Sanjeev and Barak, Boaz},
year = 2009,
publisher = cup-pub
}
@InCollection{ArzCebPer2019qap,
author = {Etor Arza} #and# Ceberio #and# {Aritz P{\'{e}}rez}
#and# Irurozki_E,
title = {Approaching the quadratic assignment problem with kernels of
mallows models under the hamming distance},
doi = {10.1145/3319619.3321976},
crossref = "GECCO2019c",
keywords = "QAP, EDA, Mallows"
}
@InCollection{AsaIwaMiy96,
author = {Y. Asahiro and K. Iwama and E. Miyano},
title = {Random Generation of Test Instances with Controlled
Attributes},
crossref = {JohTri1996},
pages = {377--393},
}
@PhDThesis{Asch95PhD,
author = Ascheuer,
title = {Hamiltonian Path Problems in the On-line
Optimization of Flexible Manufacturing Systems},
school = tub,
year = 1995,
address = {Berlin, Germany}
}
@InCollection{Atkinson00,
author = Atkinson #and# vanZyl #and# Walters #and# Savic,
title = "Genetic algorithm optimisation of level-controlled
pumping station operation",
booktitle = "Water network modelling for optimal design and
management",
pages = {79--90},
publisher = cws,
year = 2000,
}
@InCollection{AudDanOrb10,
author = Audet_C #and# Dang_CK #and# Orban_D,
title = {Algorithmic Parameter Optimization of the {DFO} Method with
the {OPAL} Framework},
crossref = "NaoTerCav2010autotun",
pages = {255--274},
}
@InCollection{AugBadBroZit2009gecco,
author = Auger_A #and# Bader_J #and# Brockhoff #and# Zitzler,
title = "Articulating User Preferences in Many-Objective
Problems by Sampling the Weighted Hypervolume",
crossref = "GECCO2009",
pages = {555--562}
}
@InCollection{AugBadBroZit2009gecco2,
author = Auger_A #and# Bader_J #and# Brockhoff #and# Zitzler,
title = "Investigating and Exploiting the Bias of the
Weighted Hypervolume to Articulate User Preferences",
crossref = "GECCO2009",
pages = {563--570}
}
@InCollection{AugBadBroZit2009hv,
title = {Theory of the hypervolume indicator: optimal
$\mu$-distributions and the choice of the reference point},
author = Auger_A #and# Bader_J #and# Brockhoff #and# Zitzler,
crossref = {GECCO2009},
pages = {87--102}
}
@InCollection{AugBroLop2012dagstuhl,
author = Auger_A #and# Brockhoff #and# Lopez-Ibanez #and# Miettinen
#and# Naujoks #and# Rudolph_G,
title = {Which questions should be asked to find the most appropriate
method for decision making and problem solving? ({Working}
{Group} ``{Algorithm} {Design} {Methods}'')},
crossref = "Dagstuhl12041",
pages = {92--93}
}
@Book{AugDoe2011,
editor = Auger_A #and# Doerr_B,
title = {Theory of Randomized Search Heuristics: Foundations and Recent Developments},
series = stcs,
volume = 1,
publisher = worldscientific,
year = 2011
}
@InProceedings{AugHan2005cec,
author = Auger_A #and# Hansen_N,
title = {A restart {CMA} evolution strategy with increasing population
size},
crossref = "CEC2005",
pages = {1769--1776},
doi = {10.1109/CEC.2005.1554902},
keywords = {IPOP-CMA-ES},
}
@InProceedings{AugHan2005lrcmaes,
author = Auger_A #and# Hansen_N,
title = {Performance evaluation of an advanced local search
evolutionary algorithm},
crossref = "CEC2005",
pages = {1777--1784},
keywords = "LR-CMAES"
}
@InCollection{AvrAllLop2021evo,
author = Avramescu_A #and# Allmendinger #and# Lopez-Ibanez,
title = "A Multi-Objective Multi-Type Facility Location Problem for
the Delivery of Personalised Medicine",
crossref = "EVOAPP2021",
pages = "388--403",
doi = "10.1007/978-3-030-72699-7_25",
abstract = "Advances in personalised medicine targeting specific
sub-populations and individuals pose a challenge to the
traditional pharmaceutical industry. With a higher level of
personalisation, an already critical supply chain is facing
additional demands added by the very sensitive nature of its
products. Nevertheless, studies concerned with the efficient
development and delivery of these products are scarce. Thus,
this paper presents the case of personalised medicine and the
challenges imposed by its mass delivery. We propose a
multi-objective mathematical model for the
location-allocation problem with two interdependent facility
types in the case of personalised medicine products. We show
its practical application through a cell and gene therapy
case study. A multi-objective genetic algorithm with a novel
population initialisation procedure is used as solution
method.",
supplement = "https://doi.org/10.5281/zenodo.4495162",
keywords = "Personalised medicine, Biopharmaceuticals Supply chain,
Facility location-allocation, Evolutionary multi-objective
optimisation",
}
@InCollection{AydYavOzyYasStu2017,
author = Dogan #and# Yavuz #and# {Serdar \"Ozy\"on and Celal Yasar}
#and# Stuetzle,
title = {Artificial Bee Colony Framework to Non-convex Economic
Dispatch Problem with Valve Point Effects: A Case Study},
pages = {1311--1318},
crossref = {GECCO2017c}
}
@InCollection{AyoAllLop2023gecco,
author = Ayodele_M #and# Allmendinger #and# Lopez-Ibanez #and# Parizy
#and# Liefooghe,
title = {Applying {Ising} Machines to Multi-Objective {QUBOs}},
pages = {2166--2174},
doi = {10.1145/3583133.3596312},
abstract = {Multi-objective optimisation problems involve finding
solutions with varying trade-offs between multiple and often
conflicting objectives. Ising machines are physical devices
that aim to find the absolute or approximate ground states of
an Ising model. To apply Ising machines to multi-objective
problems, a weighted sum objective function is used to
convert multi-objective into single-objective
problems. However, deriving scalarisation weights that
archives evenly distributed solutions across the Pareto front
is not trivial. Previous work has shown that adaptive weights
based on dichotomic search, and one based on averages of
previously explored weights can explore the Pareto front
quicker than uniformly generated weights. However, these
adaptive methods have only been applied to bi-objective
problems in the past. In this work, we extend the adaptive
method based on averages in two ways: (i) we extend the
adaptive method of deriving scalarisation weights for
problems with two or more objectives, and (ii) we use an
alternative measure of distance to improve performance. We
compare the proposed method with existing ones and show that
it leads to the best performance on multi-objective
Unconstrained Binary Quadratic Programming (mUBQP) instances
with 3 and 4 objectives and that it is competitive with the
best one for instances with 2 objectives.},
numpages = 9,
keywords = {digital annealer, multi-objective, bi-objective QAP, QUBO},
crossref = {GECCO2023c}
}
@InCollection{AyoAllLop2022gecco,
author = Ayodele_M #and# Allmendinger #and# Lopez-Ibanez #and# Parizy,
title = {Multi-Objective {QUBO} Solver: Bi-Objective Quadratic
Assignment Problem},
pages = {467--475},
doi = {10.1145/3512290.3528698},
abstract = {Quantum and quantum-inspired optimisation algorithms are
designed to solve problems represented in binary, quadratic
and unconstrained form. Combinatorial optimisation problems
are therefore often formulated as Quadratic Unconstrained
Binary Optimisation Problems (QUBO) to solve them with these
algorithms. Moreover, these QUBO solvers are often
implemented using specialised hardware to achieve enormous
speedups, e.g. Fujitsu's Digital Annealer (DA) and D-Wave's
Quantum Annealer. However, these are single-objective
solvers, while many real-world problems feature multiple
conflicting objectives. Thus, a common practice when using
these QUBO solvers is to scalarise such multi-objective
problems into a sequence of single-objective problems. Due to
design trade-offs of these solvers, formulating each
scalarisation may require more time than finding a local
optimum. We present the first attempt to extend the algorithm
supporting a commercial QUBO solver as a multi-objective
solver that is not based on scalarisation. The proposed
multi-objective DA algorithm is validated on the bi-objective
Quadratic Assignment Problem. We observe that algorithm
performance significantly depends on the archiving strategy
adopted, and that combining DA with non-scalarisation methods
to optimise multiple objectives outperforms the current
scalarised version of the DA in terms of final solution
quality.},
numpages = 9,
keywords = {digital annealer, multi-objective, bi-objective QAP, QUBO},
crossref = {GECCO2022}
}
@InCollection{AyoAllLop2022or,
author = Ayodele_M #and# Allmendinger #and# Lopez-Ibanez #and# Parizy,
title = {A Study of Scalarisation Techniques for Multi-objective
{QUBO} Solving},
pages = {393--399},
crossref = {OR2022},
doi = {10.1007/978-3-031-24907-5_47}
}
@InCollection{Ayodele2022penalty,
title = "Penalty Weights in {QUBO} Formulations: Permutation Problems",
author = Ayodele_M,
crossref = "EVOCOP2022",
pages = "159--174"
}
@InCollection{AziDoeDre2021,
author = {Aziz-Alaoui, Amine} #and# Doerr_C #and# Dreo_J,
title = {Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks},
crossref = "GECCO2021c",
pages = {1365--1374},
doi = {10.1145/3449726.3463155}
}
@Misc{BBCOMP2017,
title = {Black Box Optimization Competition},
author = Loshchilov #and# Glasmachers,
year = 2017,
url = {https://bbcomp.ini.rub.de/},
alias = "Loshchilov2017"
}
@Misc{BBOB2016bi,
author = Auger_A #and# Brockhoff #and# Hansen_N #and# {Dejan Tusar}
#and# Tusar #and# Wagner_T,
title = "{GECCO} Workshop on Real-Parameter Black-Box Optimization
Benchmarking ({BBOB} 2016): Focus on multi-objective
problems",
howpublished = {\url{https://numbbo.github.io/workshops/BBOB-2016/}},
year = 2016,
}
@InCollection{ZitLauBleu2004tutorial,
title = {A tutorial on evolutionary multiobjective optimization},
author = Zitzler #and# Laumanns #and# Bleuler,
pages = {3--37},
crossref = "MMO2004"
}
@InCollection{BLTZ2003a,
author = Bleuler #and# Laumanns #and# Thiele #and# Zitzler,
title = "{PISA} -- A Platform and Programming Language
Independent Interface for Search Algorithms ",
pages = {494--508},
crossref = "EMO2003",
}
@Misc{Bab2008spear,
author = Babic_Domagoj,
title = {Spear theorem prover},
howpublished =
{\url{https://www.domagoj-babic.com/index.php/ResearchProjects/Spear}},
year = 2008
}
@InProceedings{BabHu2007cav,
author = Babic_Domagoj #and#" Alan J. Hu",
title = {Structural Abstraction of Software Verification
Conditions},
booktitle = {Computer Aided Verification: 19th International
Conference, CAV 2007},
year = 2007,
pages = {366--378},
annote = {Spear-swv instances,
\url{http://www.cs.ubc.ca/labs/beta/Projects/ParamILS/benchmark_instances/SpearSWV/SWV-scrambled-first302.tar.gz},
\url{http://www.cs.ubc.ca/labs/beta/Projects/ParamILS/benchmark_instances/SpearSWV/SWV-scrambled-last302.tar.gz}}
}
@InProceedings{BabHut2008spear,
author = Babic_Domagoj #and# Hutter,
title = {Spear Theorem Prover},
booktitle = {SAT'08: Proceedings of the SAT 2008 Race},
year = 2008,
annote = {Unreviewed paper},
epub = {https://www.domagoj-babic.com/index.php/Pubs/SAT08},
supplement =
{https://www.domagoj-babic.com/index.php/ResearchProjects/Spear}
}
@Book{BacFogMic1997,
title = {Handbook of evolutionary computation},
author = Baeck_Thomas #and# Fogel #and# Michalewicz,
year = 1997,
publisher = iop,
}
@TechReport{BacSteWot1994tr,
author = {Achim Bachem and Barthel Steckemetz and Michael
Wottawa},
title = {An efficient parallel cluster-heuristic for large
Traveling Salesman Problems},
year = 1994,
institution = {University of Koln, Germany},
number = {94-150},
keywords = {Genetic Edge Recombination (ERX)},
}
@Book{Back1996evolutionary,
author = Baeck_Thomas,
title = {Evolutionary algorithms in theory and practice: evolution
strategies, evolutionary programming, genetic algorithms},
year = 1996,
publisher = oup
}
@InCollection{BalBirStu06,
author = Balaprakash #and# Birattari #and# Stuetzle #and# Dorigo,
title = {Incremental local search in ant colony optimization:
Why it fails for the quadratic assignment problem},
pages = {156--166},
crossref = "ANTS2006"
}
@InCollection{BalBirStu07,
author = Balaprakash #and# Birattari #and# Stuetzle,
title = {Improvement Strategies for the {F}-Race Algorithm:
Sampling Design and Iterative Refinement},
pages = {108--122},
crossref = "HM2007",
keywords = {Iterated Race},
doi = {10.1007/978-3-540-75514-2_9}
}
@InCollection{BalHo1980,
author = Balas #and# {Andrew Ho},
title = {Set Covering Algorithms Using Cutting Planes, Heuristics, and
Subgradient Optimization: A Computational Study},
booktitle = "Combinatorial optimization",
series = mps,
year = 1980,
volume = 12,
publisher = springer,
address = add-berlin-heidelberg,
pages = {37--60},
editor = "Padberg, M. W.",
doi = "10.1007/BFb0120886"
}
@InProceedings{BapHgu1997,
author = {P. Baptiste} #and# {L. K. Hguny},
title = {A branch and bound algorithm for the F$/$no\_idle$/C_\text{max}$},
booktitle = {Proceedings of the international conference on industrial engineering and production management, IEPM'97},
year = 1997,
address = {Lyon},
pages = {429--438}
}
@Book{Bar2006newexp,
author = Bartz-Beielstein,
title = {Experimental Research in Evolutionary Computation:
The New Experimentalism},
publisher = springer,
year = 2006,
address = add-berlin,
keywords = {SPO}
}
@InCollection{Bar2015genera,
author = Bartz-Beielstein,
title = {How to Create Generalizable Results},
pages = {1127--1142},
crossref = "HandbookCI2015",
keywords = "Mixed-effects models, random-effects model, problem instance
generation"
}
@InProceedings{BarFlaKocKon2010spot,
title = {{SPOT}: A Toolbox for Interactive and Automatic Tuning in the
\proglang{R} Environment},
author = Bartz-Beielstein #and# {Flasch, Oliver and Koch, Patrick
and Konen, Wolfgang},
booktitle = {Proceedings 20. Workshop Computational Intelligence},
year = 2010,
address = {Karlsruhe},
publisher = {KIT Scientific Publishing},
alias = "Bartz-Beielstein2010",
pages = {264--273}
}
@InProceedings{BarLasPre2005cec,
author = Bartz-Beielstein #and# Lasarczyk #and# Preuss_M,
title = {Sequential Parameter Optimization},
pages = {773--780},
crossref = "CEC2005",
}
@InCollection{BarLasPre2010emaoa,
author = Bartz-Beielstein #and# Lasarczyk #and# Preuss_M,
crossref = "BarChiPaqPre2010emaoa",
title = "The Sequential Parameter Optimization Toolbox",
pages = {337--360},
keywords = {SPOT},
doi = "10.1007/978-3-642-02538-9_14"
}
@InProceedings{BarMar2004,
title = {Tuning search algorithms for real-world applications: A
regression tree based approach},
author = Bartz-Beielstein #and# {Markon, Sandor},
pages = {1111--1118},
crossref = {CEC2004}
}
@InProceedings{BarPea2012aaai,
title = {Transportability of causal effects: Completeness results},
author = Bareinboim_E #and# Pearl_J,
crossref = {AAAI2012},
pages = {698,704}
}
@InProceedings{BarPre2005em,
author = Bartz-Beielstein #and# Preuss_M,
title = "Considerations of budget allocation for sequential parameter
optimization ({SPO})",
pages = {35--40},
crossref = {EMAA2006}
}
@InCollection{BarPre2014experimental,
author = Bartz-Beielstein #and# Preuss_M,
title = "Experimental Analysis of Optimization Algorithms: Tuning and
Beyond",
crossref = "BorMor2014theory",
doi = "10.1007/978-3-642-33206-7_10",
pages = "205--245"
}
@InProceedings{BarSch03,
author = Baran #and# Schaerer,
title = {A multiobjective ant colony system for vehicle
routing problem with time windows},
booktitle = {Proceedings of the Twenty-first IASTED International
Conference on Applied Informatics},
pages = {97--102},
year = 2003,
address = {Insbruck, Austria}
}
@InCollection{BasGoeLie2013gecco,
author = {Basseur, Matthieu} #and# Goeffon #and# Liefooghe #and# Verel,
title = {On Set-based Local Search for Multiobjective Combinatorial
Optimization},
crossref = "GECCO2013",
pages = {471--478},
doi = {10.1145/2463372.2463430},
acmid = 2463430,
}
@InCollection{BasYevDeuEmm2017,
author = {Basto-Fernandes, Vitor and Yevseyeva, Iryna} #and# Deutz_A
#and# Emmerich,
title = {A survey of diversity oriented optimization: Problems,
indicators, and algorithms},
pages = {3--23},
crossref = {EVOLVE2017},
doi = {10.1007/978-3-319-49325-1_1}
}
@Book{BatBruMas08:book,
author = Battiti #and# "M. Brunato" #and# Mascia_F,
title = {Reactive Search and Intelligent Optimization},
publisher = springer,
address = add-ny,
year = 2008,
series = {Operations Research/Computer Science Interfaces},
volume = 45,
doi = {10.1007/978-0-387-09624-7},
}
@InProceedings{BatCam2009reactive,
title = {Reactive search optimization: Learning while optimizing. An
experiment in interactive multi-objective optimization},
author = Battiti #and# Campigotto_P,
crossref = "MIC2009",
}
@InProceedings{BatSchUrl2014patat,
author = {Michele Battistutta} #and# Schaerf #and# Urli,
title = {Feature-based tuning of single-stage simulated annealing for examination timetabling},
crossref = {PATAT2014},
pages = {53--61},
keywords = {F-race},
}
@Unpublished{Bau1986a,
author = {E. B. Baum},
title = {Iterated Descent: A Better Algorithm for Local
Search in Combinatorial Optimization Problems},
note = {Manuscript},
year = 1986
}
@InProceedings{Bau1986b,
author = {E. B. Baum},
title = {Towards Practical ``Neural'' Computation for Combinatorial
Optimization Problems},
booktitle = {Neural Networks for Computing, AIP Conference Proceedings},
year = 1986,
pages = {53--64}
}
@InProceedings{BayDerSab2005cie,
author = {A. Baykasoglu and T. Dereli and I. Sabuncu},
title = {A multiple objective ant colony optimization
approach to assembly line balancing problems},
booktitle = {35th International Conference on Computers and