% References should come from https://iridia-ulb.github.io/references/



@article{Jen03,
  title = {Reducing the run-time complexity of multiobjective
                  {EA}s: The {NSGA-II} and other algorithms},
  author = {M. T. Jensen},
  journal = {IEEE Transactions on Evolutionary Computation},
  volume = 7,
  number = 5,
  pages = {503--515},
  year = 2003
}

@article{Deb02nsga2,
  author = { Kalyanmoy Deb  and A. Pratap and S. Agarwal and T. Meyarivan},
  title = {A fast and elitist multi-objective genetic
                  algorithm: {NSGA-II}},
  journal = {IEEE Transactions on Evolutionary Computation},
  year = 2002,
  volume = 6,
  number = 2,
  pages = {182--197},
  doi = {10.1109/4235.996017}
}

@incollection{FonGueLopPaq2011emo,
  address = { Heidelberg },
  publisher = {Springer},
  year = 2011,
  series = {Lecture Notes in Computer Science},
  volume = 6576,
  editor = { Takahashi, R. H. C.  and others},
  booktitle = { Evolutionary Multi-criterion Optimization, EMO 2011},
  author = { Carlos M. Fonseca  and  Andreia P. Guerreiro  and  Manuel L{\'o}pez-Ib{\'a}{\~n}ez  and  Lu{\'i}s Paquete },
  title = {On the Computation of the Empirical Attainment Function},
  doi = {10.1007/978-3-642-19893-9_8},
  pages = {106--120},
  abstract = {The attainment function provides a description of the
                  location of the distribution of a random non-dominated point
                  set. This function can be estimated from experimental data
                  via its empirical counterpart, the empirical attainment
                  function (EAF). However, computation of the EAF in more than
                  two dimensions is a non-trivial task. In this article, the
                  problem of computing the empirical attainment function is
                  formalised, and upper and lower bounds on the corresponding
                  number of output points are presented. In addition, efficient
                  algorithms for the two and three-dimensional cases are
                  proposed, and their time complexities are related to lower
                  bounds derived for each case.}
}

@article{DiaLop2020ejor,
  author =       { Juan Esteban Diaz and Manuel L{\'o}pez-Ib{\'a}ñez },
  title =        {Incorporating Decision-Maker's Preferences into the Automatic
                  Configuration of Bi-Objective Optimisation Algorithms},
  journal =      {European Journal of Operational Research},
  year =         2021,
  volume =       289,
  number =       3,
  pages =        {1209--1222},
  doi =          {10.1016/j.ejor.2020.07.059},
  abstract =     {Automatic configuration (AC) methods are increasingly used to
                  tune and design optimisation algorithms for problems with
                  multiple objectives. Most AC methods use unary quality
                  indicators, which assign a single scalar value to an
                  approximation to the Pareto front, to compare the performance
                  of different optimisers. These quality indicators, however,
                  imply preferences beyond Pareto-optimality that may differ
                  from those of the decision maker (DM). Although it is
                  possible to incorporate DM's preferences into quality
                  indicators, e.g., by means of the weighted hypervolume
                  indicator (HV$^w$), expressing preferences in terms of weight
                  function is not always intuitive nor an easy task for a DM,
                  in particular, when comparing the stochastic outcomes of
                  several algorithm configurations. A more visual approach to
                  compare such outcomes is the visualisation of their empirical
                  attainment functions (EAFs) differences. This paper proposes
                  using such visualisations as a way of eliciting information
                  about regions of the objective space that are preferred by
                  the DM. We present a method to convert the information about
                  EAF differences into a HV$^w$ that will assign higher quality
                  values to approximation fronts that result in EAF differences
                  preferred by the DM. We show that the resulting HV$^w$ may be
                  used by an AC method to guide the configuration of
                  multi-objective optimisers according to the preferences of
                  the DM. We evaluate the proposed approach on a well-known
                  benchmark problem. Finally, we apply our approach to
                  re-configuring, according to different DM's preferences, a
                  multi-objective optimiser tackling a real-world production
                  planning problem arising in the manufacturing industry.},
  supplement =   {https://doi.org/10.5281/zenodo.3749288}
}

@incollection{Grunert01,
  year = 2001,
  series = {Lecture Notes in Computer Science},
  volume = 1993,
  publisher = {Springer, Heidelberg, Germany},
  editor = { Eckart Zitzler  and  Kalyanmoy Deb  and  Lothar Thiele  and  Carlos A. {Coello Coello}  and  David Corne },
  booktitle = {Evolutionary Multi-criterion Optimization, EMO 2001},
  author = { Viviane {Grunert da Fonseca}  and  Carlos M. Fonseca  and  Andreia O. Hall },
  key = {Fonseca et al., 2001},
  title = {Inferential Performance Assessment of Stochastic Optimisers
                  and the Attainment Function},
  pages = {213--225},
  alias = {Fonseca01},
  doi = {10.1007/3-540-44719-9_15},
  annote = {Proposed looking at anytime behavior as a multi-objective
                  problem},
  keywords = {EAF}
}

@phdthesis{LopezIbanezPhD,
  author = { Manuel L{\'o}pez-Ib{\'a}ñez },
  title = {Operational Optimisation of Water Distribution
                  Networks},
  school = {School of Engineering and the Built Environment},
  year = 2009,
  address = {Edinburgh Napier University, UK},
  url = {https://researchrepository.napier.ac.uk/id/eprint/3044}
}

@incollection{GruFon2009:emaa,
  editor = { Thomas Bartz-Beielstein  and  Marco Chiarandini  and  Lu{\'i}s Paquete  and  Mike Preuss },
  year = 2010,
  address = {Berlin, Germany},
  publisher = {Springer},
  booktitle = {Experimental Methods for the Analysis of
                  Optimization Algorithms},
  author = { Viviane {Grunert da Fonseca}  and  Carlos M. Fonseca },
  title = {The Attainment-Function Approach to Stochastic
                  Multiobjective Optimizer Assessment and Comparison},
  pages = {103--130}
}



@incollection{LopPaqStu09emaa,
  editor = { Thomas Bartz-Beielstein  and  Marco Chiarandini  and  Lu{\'i}s Paquete  and  Mike Preuss },
  year = 2010,
  address = {Berlin, Germany},
  publisher = {Springer},
  booktitle = {Experimental Methods for the Analysis of
                  Optimization Algorithms},
  author = { Manuel L{\'o}pez-Ib{\'a}ñez  and  Lu{\'i}s Paquete  and  Thomas St{\"u}tzle },
  title = {Exploratory Analysis of Stochastic Local Search
                  Algorithms in Biobjective Optimization},
  pages = {209--222},
  doi = {10.1007/978-3-642-02538-9_9},
  abstract = {This chapter introduces two Perl programs that
                  implement graphical tools for exploring the
                  performance of stochastic local search algorithms
                  for biobjective optimization problems. These tools
                  are based on the concept of the empirical attainment
                  function (EAF), which describes the probabilistic
                  distribution of the outcomes obtained by a
                  stochastic algorithm in the objective space. In
                  particular, we consider the visualization of
                  attainment surfaces and differences between the
                  first-order EAFs of the outcomes of two
                  algorithms. This visualization allows us to identify
                  certain algorithmic behaviors in a graphical way.
                  We explain the use of these visualization tools and
                  illustrate them with examples arising from
                  practice.}
}

@article{BinGinRou2015gaupar,
  title = {Quantifying uncertainty on {P}areto fronts with {G}aussian
                  process conditional simulations},
  volume = 243,
  doi = {10.1016/j.ejor.2014.07.032},
  abstract = {Multi-objective optimization algorithms aim at finding
                  Pareto-optimal solutions. Recovering Pareto fronts or Pareto
                  sets from a limited number of function evaluations are
                  challenging problems. A popular approach in the case of
                  expensive-to-evaluate functions is to appeal to
                  metamodels. Kriging has been shown efficient as a base for
                  sequential multi-objective optimization, notably through
                  infill sampling criteria balancing exploitation and
                  exploration such as the Expected Hypervolume
                  Improvement. Here we consider Kriging metamodels not only for
                  selecting new points, but as a tool for estimating the whole
                  Pareto front and quantifying how much uncertainty remains on
                  it at any stage of Kriging-based multi-objective optimization
                  algorithms. Our approach relies on the Gaussian process
                  interpretation of Kriging, and bases upon conditional
                  simulations. Using concepts from random set theory, we
                  propose to adapt the Vorob'ev expectation and deviation to
                  capture the variability of the set of non-dominated
                  points. Numerical experiments illustrate the potential of the
                  proposed workflow, and it is shown on examples how Gaussian
                  process simulations and the estimated Vorob'ev deviation can
                  be used to monitor the ability of Kriging-based
                  multi-objective optimization algorithms to accurately learn
                  the Pareto front.},
  number = 2,
  journal = {European Journal of Operational Research},
  author = {Binois, M. and Ginsbourger, D. and Roustant, O.},
  year = 2015,
  keywords = {Attainment function, Expected Hypervolume Improvement,
                  Kriging, Multi-objective optimization, Vorob'ev expectation},
  pages = {386--394}
}

@phdthesis{ChiarandiniPhD,
  author = { Marco Chiarandini },
  title = {Stochastic Local Search Methods for Highly
                  Constrained Combinatorial Optimisation Problems},
  school = {FB Informatik, TU Darmstadt, Germany},
  year = 2005
}

@article{JohAraMcGSch1991,
  author = {David S. Johnson and Cecilia R. Aragon and  Lyle A. McGeoch  and Catherine Schevon},
  title = {Optimization by Simulated Annealing: An Experimental
                  Evaluation: Part {II}, Graph Coloring and Number Partitioning},
  journal = {Operations Research},
  year = 1991,
  volume = 39,
  number = 3,
  pages = {378--406}
}

@incollection{FonPaqLop06:hypervolume,
  address = {Piscataway, NJ},
  publisher = {IEEE Press},
  month = jul,
  year = 2006,
  booktitle = {Proceedings of the 2006 Congress on Evolutionary
                  Computation (CEC 2006)},
  key = {IEEE CEC},
  author = { Carlos M. Fonseca  and  Lu{\'i}s Paquete  and  Manuel L{\'o}pez-Ib{\'a}ñez },
  title = {An improved dimension-sweep
                  algorithm for the hypervolume indicator},
  pages = {1157--1163},
  doi = {10.1109/CEC.2006.1688440},
  pdf = {FonPaqLop06-hypervolume.pdf},
}

@article{BeuFonLopPaqVah09:tec,
  author = { Nicola Beume  and  Carlos M. Fonseca  and  Manuel L{\'o}pez-Ib{\'a}ñez  and  Lu{\'i}s Paquete  and  Jan Vahrenhold },
  title = {On the complexity of computing the hypervolume
                  indicator},
  journal = {IEEE Transactions on Evolutionary Computation},
  year = 2009,
  volume = 13,
  number = 5,
  pages = {1075--1082},
  doi = {10.1109/TEVC.2009.2015575},
}



@article{ZitThiLauFon2003:tec,
  author = { Eckart Zitzler  and  Lothar Thiele  and  Marco Laumanns  and  Carlos M. Fonseca  and Viviane {Grunert da Fonseca}},
  title = {Performance Assessment of Multiobjective Optimizers:
                  an Analysis and Review},
  journal = {IEEE Transactions on Evolutionary Computation},
  year = 2003,
  volume = 7,
  number = 2,
    pages = {117--132},
  alias = {perfassess}
}



@incollection{BezLopStu2017emo,
  editor = {Heike Trautmann and G{\"{u}}nter Rudolph and Kathrin Klamroth
                  and Oliver Sch{\"{u}}tze and Margaret M. Wiecek and Yaochu
                  Jin and Christian Grimme},
  year = 2017,
  series = {Lecture Notes in Computer Science},
  address = {Cham, Switzerland},
  publisher = {Springer International Publishing},
  booktitle = {Evolutionary Multi-criterion Optimization, EMO 2017},
  author = { Leonardo C. T. Bezerra  and  Manuel L{\'o}pez-Ib{\'a}ñez  and  Thomas St{\"u}tzle },
  title = {An Empirical Assessment of the Properties of Inverted
                  Generational Distance Indicators on Multi- and Many-objective
                  Optimization},
  pages = {31--45},
  doi = {10.1007/978-3-319-54157-0_3}
}

@incollection{CoeSie2004igd,
  publisher = {Springer, Heidelberg, Germany},
  volume = 2972,
  series = {Lecture Notes in Artificial Intelligence},
  booktitle = {Proceedings of MICAI},
  editor = {Monroy, Ra{\'u}l and Arroyo-Figueroa, Gustavo and Sucar, Luis
                  Enrique and Sossa, Humberto},
  author = { Carlos A. {Coello Coello}  and Reyes-Sierra, Margarita},
  title = {A Study of the Parallelization of a Coevolutionary
                  Multi-objective Evolutionary Algorithm},
  year = 2004,
  pages = {688--697},
  keywords = {IGD},
  annote = {Introduces Inverted Generational Distance (IGD)}
}

@incollection{IshMasTanNoj2015igd,
  editor = { Ant{\'o}nio Gaspar{-}Cunha  and Carlos Henggeler Antunes and  Carlos A. {Coello Coello} },
  volume = 9018,
  year = 2015,
  series = {Lecture Notes in Computer Science},
  publisher = {Springer, Heidelberg, Germany},
  booktitle = {Evolutionary Multi-criterion Optimization, EMO 2015 Part {I}},
  author = { Ishibuchi, Hisao  and Masuda, Hiroyuki and Tanigaki, Yuki and
                  Nojima, Yusuke},
  title = {Modified Distance Calculation in Generational Distance and
                  Inverted Generational Distance},
  pages = {110--125}
}

@article{SchEsqLarCoe2012tec,
  author = { Oliver Sch{\"u}tze  and X. Esquivel and A. Lara and  Carlos A. {Coello Coello} },
  journal = {IEEE Transactions on Evolutionary Computation},
  title = {Using the Averaged {Hausdorff} Distance as a Performance
                  Measure in Evolutionary Multiobjective Optimization},
  year = 2012,
  volume = 16,
  number = 4,
  pages = {504--522}
}

@inproceedings{VelLam1998gp,
  year = 1998,
  publisher = {Stanford University Bookstore},
  address = {Stanford University, California},
  month = jul,
  editor = {John R. Koza},
  booktitle = {Late Breaking Papers at the Genetic Programming 1998
                  Conference},
  alias = {Veldhuizen98a},
  key = {Van Veldhuizen and Lamont, 1998a},
  title = {Evolutionary Computation and Convergence to a
                  {P}areto Front},
  author = { David A. {Van Veldhuizen}  and  Gary B. Lamont },
  pages = {221--228},
  keywords = {generational distance}
}

@incollection{AugBadBroZit2009gecco,
  address = {New York, NY},
  publisher = {ACM Press},
  year = 2009,
  editor = {Franz Rothlauf},
  booktitle = {Proceedings of the Genetic and Evolutionary
                  Computation Conference, GECCO 2009},
  author = { Anne Auger  and  Johannes Bader  and  Dimo Brockhoff  and  Eckart Zitzler },
  title = {Articulating User Preferences in Many-Objective
                  Problems by Sampling the Weighted Hypervolume},
  pages = {555--562}
}

@article{ZhoZhaJin2009igdx,
  author = "Zhou, A. and  Zhang, Qingfu  and  Yaochu Jin ",
  title = {Approximating the set of {Pareto}-optimal solutions in both
                  the decision and objective spaces by an estimation of
                  distribution algorithm},
  journal = {IEEE Transactions on Evolutionary Computation},
  year = 2009,
  volume = 13,
  number = 5,
  pages = {1167--1189},
  doi = {10.1109/TEVC.2009.2021467},
  keywords = {multi-modal, IGDX}
}