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@inproceedings{gpruvost_gecco2020,
author = {Pruvost, Geoffrey and Derbel, Bilel and Liefooghe, Arnaud and Verel, Sebastien and Zhang, Qingfu},
title = {Surrogate-Assisted Multi-Objective Combinatorial Optimization Based on Decomposition and Walsh Basis},
year = {2020},
isbn = {9781450371285},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3377930.3390149},
doi = {10.1145/3377930.3390149},
booktitle = {Proceedings of the 2020 Genetic and Evolutionary Computation Conference},
pages = {542–550},
numpages = {9},
keywords = {discrete surrogates, decomposition, multi-objective optimization},
location = {Canc\'{u}n, Mexico},
series = {GECCO ’20}
}

@InProceedings{gpruvost_evocop2020,
author={Pruvost, Geoffrey and Derbel, Bilel and Liefooghe, Arnaud and Li, Ke and Zhang, Qingfu},
editor={Paquete, Lu{\'i}s and Zarges, Christine},
title={On the Combined Impact of Population Size and Sub-problem Selection in MOEA/D},
booktitle={Evolutionary Computation in Combinatorial Optimization},
year={2020},
publisher={Springer International Publishing},
address={Cham},
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}

@article{Campelo_2020,
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@ARTICLE{pymoo,
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doi = {10.1145/2739482.2768462},
abstract = {jMetal, an open source, Java-based framework for multi-objective optimization with metaheuristics, has become a valuable tool for many researches in the area as well as for some industrial partners in the last ten years. Our experience using and maintaining it during that time, as well as the received comments and suggestions, have helped us improve the jMetal design and identify significant features to incorporate. This paper revisits the jMetal architecture, describing its refined new design, which relies on design patterns, principles from object-oriented design, and a better use of the Java language features to improve the quality of the code, without disregarding jMetal ever goals of simplicity, facility of use, flexibility, extensibility and portability. Among the newly incorporated features, jMetal supports live interaction with running algorithms and parallel execution of algorithms.},
booktitle = {Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation},
pages = {1093–1100},
numpages = {8},
keywords = {open source, multi-objective metaheu- ristics, optimization framework, jmetal},
location = {Madrid, Spain},
series = {GECCO Companion '15}
}

@INPROCEEDINGS{vanrijn2016,
    author={S. {van Rijn} and H. {Wang} and M. {van Leeuwen} and T. {Bäck}},
    booktitle={2016 IEEE Symposium Series on Computational Intelligence (SSCI)},
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}

@article{DEAP_JMLR2012,
    author    = " F\'elix-Antoine Fortin and Fran\c{c}ois-Michel {De Rainville} and Marc-Andr\'e Gardner and Marc Parizeau and Christian Gagn\'e ",
    title     = { {DEAP}: Evolutionary Algorithms Made Easy },
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    month     = { jul },
    year      = { 2012 },
    journal   = { Journal of Machine Learning Research }
}