Theory of the hypervolume indicator: optimal µ-distributions and the choice of the reference point, Foundations of Genetic Algorithms, pp.87-102, 2009. ,
URL : https://hal.archives-ouvertes.fr/inria-00430540
Hypervolume-based multiobjective optimization: Theoretical foundations and practical implications, Theoretical Computer Science, vol.425, pp.75-103, 2012. ,
DOI : 10.1016/j.tcs.2011.03.012
URL : https://hal.archives-ouvertes.fr/inria-00638989
Set-based Multi-objective Optimization, Indicators, and Deteriorative Cycles, Genetic and Evolutionary Computation Conference, pp.495-502, 2010. ,
DOI : 10.1145/1830483.1830574
SMS-EMOA: Multiobjective Selection Based on Dominated Hypervolume, European Journal of Operational Research, vol.181, pp.1653-1669, 2007. ,
DOI : 10.1016/j.ejor.2006.08.008
Convergence of hypervolume-based archiving algorithms I: Effectiveness, Proceedings of the 13th annual conference on Genetic and evolutionary computation, pp.745-752, 2011. ,
DOI : 10.1145/2330163.2330229
Regret analysis of stochastic and nonstochastic multi-armed bandit problems, Foundations and Trends® in Machine Learning, vol.5, pp.1-122, 2012. ,
DOI : 10.1561/2200000024
URL : http://arxiv.org/pdf/1204.5721.pdf
On Object-Oriented Programming of OptimizersExamples in Scilab, Multidisciplinary Design Optimization in Computational Mechanics, pp.499-538, 2010. ,
A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, vol.6, pp.182-197, 2002. ,
DOI : 10.1109/4235.996017
An EMO algorithm using the hypervolume measure as selection criterion, International Conference on Evolutionary Multi-Criterion Optimization, pp.62-76, 2005. ,
DOI : 10.1007/978-3-540-31880-4_5
The computation of the expected improvement in dominated hypervolume of Pareto front approximations, 2008. ,
Genetic Algorithms in Search, Optimization, and Machine Learning, 1989. ,
Evaluating The Quality of Approximations of the Non-Dominated Set, 1998. ,
CMA-ES/pycma on Github, 2019. ,
DOI : 10.1145/2001858.2002123
Evolution strategies, Springer handbook of computational intelligence, pp.871-898, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01155533
Completely Derandomized Self-Adaptation in Evolution Strategies, Evolutionary Computation, vol.9, pp.159-195, 2001. ,
DOI : 10.1162/106365601750190398
URL : https://www.mitpressjournals.org/userimages/ContentEditor/1164817256746/lib_rec_form.pdf
The Set-Based Hypervolume Newton Method for Bi-Objective Optimization, IEEE transactions on cybernetics in print, 2018. ,
Covariance matrix adaptation for multiobjective optimization, Evolutionary Computation, vol.15, pp.1-28, 2007. ,
Statistical improvement criteria for use in multiobjective design optimization, AIAA journal, vol.44, issue.4, pp.879-891, 2006. ,
DOI : 10.2514/1.16875
A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers, 2006. ,
Qualitative and quantitative assessment of step size adaptation rules, Proceedings of the 14th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, pp.139-148, 2017. ,
DOI : 10.1145/3040718.3040725
, Nonlinear Multiobjective Optimization. Kluwer, 1999.
Multiobjective Optimization on a Limited Budget of Evaluations Using ModelAssisted S-Metric Selection. In Parallel Problem Solving from, Nature, 2008. ,
, , pp.784-794
On BiObjective convex-quadratic problems, International Conference on Evolutionary Multi-Criterion Optimization, pp.3-14, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-01942159
Improved Step Size Adaptation for the MO-CMA-ES, Genetic and Evolutionary Computation Conference, pp.487-494, 2010. ,
On expected-improvement criteria for model-based multi-objective optimization, International Conference on Parallel Problem Solving from Nature, pp.718-727, 2010. ,
Online convergence detection for evolutionary multi-objective algorithms revisited, IEEE Congress on Evolutionary Computation, pp.1-8, 2010. ,
evoalgos: Modular evolutionary algorithms. Python package version 1, 2017. ,
MultiObjective Bayesian Global Optimization using expected hypervolume improvement gradient, Swarm and evolutionary computation, vol.44, pp.945-956, 2019. ,
DOI : 10.1016/j.swevo.2018.10.007
URL : https://doi.org/10.1016/j.swevo.2018.10.007
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition, IEEE Transactions on Evolutionary Computation, vol.11, pp.712-731, 2007. ,
Indicator-based selection in multiobjective search, International Conference on Parallel Problem Solving from Nature, pp.832-842, 2004. ,
Multiobjective Optimization Using Evolutionary Algorithms -A Comparative Case Study, Conference on Parallel Problem Solving from Nature (PPSN V), vol.1498, pp.292-301, 1998. ,
Multiobjective optimization using evolutionary algorithms -A comparative case study, International conference on parallel problem solving from nature, pp.292-301, 1998. ,