{"type":"ScholarlyArticle","title":"Dynamic Many-Objective Molecular Optimization: Unfolding Complexity with Objective Decomposition and Progressive Optimization","authors":[{"name":"Dong-Hee Shin","slug":"dong-hee-shin","given":"Dong-Hee","family":"Shin","affiliation":"Department of Artificial Intelligence , Korea University {dongheeshin ,"},{"name":"Young-Han Son","slug":"young-han-son","given":"Young-Han","family":"Son","affiliation":"Department of Artificial Intelligence , Korea University {dongheeshin ,"},{"name":"Deok-Joong Lee","slug":"deok-joong-lee","given":"Deok-Joong","family":"Lee","affiliation":"Department of Artificial Intelligence , Korea University {dongheeshin ,"},{"name":"Ji-Wung Han","slug":"ji-wung-han","given":"Ji-Wung","family":"Han","affiliation":"Department of Artificial Intelligence , Korea University {dongheeshin ,"},{"name":"Tae-Eui Kam","slug":"tae-eui-kam","given":"Tae-Eui","family":"Kam","affiliation":"Department of Artificial Intelligence , Korea University {dongheeshin ,"}],"abstract":"Molecular discovery has received significant attention across various scientific fields by enabling the creation of novel chemical compounds. In recent years, the majority of studies have approached this process as a multi-objective optimization problem. Despite notable advancements, most methods optimize only up to four molecular objectives and are mainly designed for scenarios with a predetermined number of objectives. However, in real-world applications, the number of molecular objectives can be more than four (many-objective) and additional objectives may be introduced over time (dynamic-objective). To fill this gap, we propose DyMol, the first method designed to tackle the dynamic many-objective molecular optimization problem by utilizing a novel divide-and-conquer approach combined with a decomposition strategy. Additionally, we comprehensively integrate convergence, Pareto diversity, and structural diversity into the optimization process to provide efficient exploration of the search space. We validate the superior performance of our method using the practical molecular optimization (PMO) benchmark. The source code and supplementary material are available online.","doi":"10.24963/ijcai.2024/666","url":"https://ijcai.org/proceedings/2024/666","pdf":"https://ijcai.org/proceedings/2024/0666.pdf","markdown":"https://ijcai.org/proceedings/2024/666.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/666","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":666,"pages":{"first":6026,"last":6034},"keywords":["Multidisciplinary Topics and Applications: MTA: Health and medicine","Multidisciplinary Topics and Applications: MTA: Bioinformatics"],"videos":[],"references":[{"position":1,"raw":"[Aldeghi et al., 2022] Matteo Aldeghi, David E Graff, Nathan Frey, Joseph A Morrone, Edward O Pyzer-Knapp, Kirk E Jordan, and Connor W Coley. Roughness of molecular property landscapes and its impact on modella- bility. Journal of Chemical Information and Modeling, 62(19):4660-4671, 2022.","title":"Roughness of molecular property landscapes and its impact on modellability","authors":"Matteo Aldeghi, David E Graff, Nathan Frey, Joseph A Morrone, Edward O Pyzer-Knapp, Kirk E Jordan, Connor W Coley","year":"2022","venue":"Journal of Chemical Information and Modeling","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bemis and Murcko, 1996] Guy W Bemis and Mark A Mur- cko. The properties of known drugs. 1. molecular frame- works. Journal of medicinal chemistry, 39(15):2887- 2893, 1996.","title":"The properties of known drugs. 1. molecular frameworks","authors":"W Guy, Mark A Bemis, Murcko","year":"1996","venue":"Journal of medicinal chemistry","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bengio et al., 2021] Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, and Yoshua Bengio. Flow network based generative models for non-iterative diverse candidate generation. Advances in Neural Information Processing Systems, 34:27381-27394, 2021.","title":"Flow network based generative models for non-iterative diverse candidate generation","authors":"Emmanuel Bengio, Moksh Jain, Maksym Korablyov, Doina Precup, Yoshua Bengio","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bilodeau et al., 2022] Camille Bilodeau, Wengong Jin, Tommi Jaakkola, Regina Barzilay, and Klavs F Jensen. Generative models for molecular discovery: Recent ad- vances and challenges. Wiley Interdisciplinary Reviews: Computational Molecular Science, 12(5):e1608, 2022.","title":"Generative models for molecular discovery: Recent advances and challenges","authors":"Camille Bilodeau, Wengong Jin, Tommi Jaakkola, Regina Barzilay, Klavs F Jensen","year":"2022","venue":"Wiley Interdisciplinary Reviews: Computational Molecular Science","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Brattka et al., 2012] Vasco Brattka, Guido Gherardi, and Alberto Marcone. The Bolzano-Weierstrass theorem is the jump of weak kőnig's lemma. Annals of Pure and Applied Logic, 163(6):623-655, 2012.","title":"The Bolzano-Weierstrass theorem is the jump of weak kőnig's lemma","authors":"Vasco Brattka, Guido Gherardi, Alberto Marcone","year":"2012","venue":"Annals of Pure and Applied Logic","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Brockhoff et al., 2012] Dimo Brockhoff, Tobias Wagner, and Heike Trautmann. On the properties of the r2 indica- tor. In Proceedings of the 14th annual conference on Ge- netic and evolutionary computation, pages 465-472, 2012.","title":"On the properties of the r2 indicator","authors":"Dimo Brockhoff, Tobias Wagner, Heike Trautmann","year":"2012","venue":"Proceedings of the 14th annual conference on Genetic and evolutionary computation","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Darrow et al., 2020] Jonathan J Darrow, Jerry Avorn, and Aaron S Kesselheim. Fda approval and regulation of phar- maceuticals, 1983-2018. Jama, 323(2):164-176, 2020.","title":"Fda approval and regulation of pharmaceuticals, 1983-2018","authors":"Jonathan J Darrow, Jerry Avorn, Aaron S Kesselheim","year":"2020","venue":"Jama","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Deb and Jain, 2013] Kalyanmoy Deb and Himanshu Jain. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting ap- proach, part i: solving problems with box con- straints. IEEE transactions on evolutionary computation, 18(4):577-601, 2013.","title":"An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints","authors":"Deb, Jain ; ] Kalyanmoy, Deb, Himanshu Jain","year":"2013","venue":"IEEE transactions on evolutionary computation","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Ding et al., 2018] Jinliang Ding, Cuie Yang, Qiong Xiao, Tianyou Chai, and Yaochu Jin. Dynamic evolutionary multiobjective optimization for raw ore allocation in min- eral processing. IEEE Transactions on Emerging Topics in Computational Intelligence, 3(1):36-48, 2018.","title":"Dynamic evolutionary multiobjective optimization for raw ore allocation in mineral processing","authors":"Ding","year":"2018","venue":"IEEE Transactions on Emerging Topics in Computational Intelligence","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Eichfelder, 2009] Gabriele Eichfelder. Scalarizations for adaptively solving multi-objective optimization problems. Computational Optimization and Applications, 44:249- 273, 2009.","title":"Scalarizations for adaptively solving multi-objective optimization problems","authors":"Gabriele Eichfelder","year":"2009","venue":"Computational Optimization and Applications","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Fromer and Coley, 2023] Jenna C Fromer and Connor W Coley. Computer-aided multi-objective optimization in small molecule discovery. Patterns, 4(2), 2023.","title":"Computer-aided multi-objective optimization in small molecule discovery","authors":"C Jenna, Connor W Fromer, Coley","year":"2023","venue":"Patterns","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Fu et al., 2021] Tianfan Fu, Cao Xiao, Xinhao Li, Lu- cas M Glass, and Jimeng Sun. Mimosa: Multi-constraint molecule sampling for molecule optimization. In Proceed- ings of the AAAI Conference on Artificial Intelligence, vol- ume 35, pages 125-133, 2021.","title":"Mimosa: Multi-constraint molecule sampling for molecule optimization","authors":"Tianfan Fu, Cao Xiao, Xinhao Li, Lucas M Glass, Jimeng Sun","year":"2021","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Gao et al., 2022] Wenhao Gao, Tianfan Fu, Jimeng Sun, and Connor Coley. Sample efficiency matters: a bench- mark for practical molecular optimization. Advances in Neural Information Processing Systems, 35:21342-21357, 2022.","title":"Sample efficiency matters: a benchmark for practical molecular optimization","authors":"Gao","year":"2022","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Gómez-Bombarelli et al., 2018] Rafael Gómez- Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez- Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru- Guzik. Automatic chemical design using a data-driven continuous representation of molecules. ACS central science, 4(2):268-276, 2018.","title":"Automatic chemical design using a data-driven continuous representation of molecules","authors":"Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, Alán Aspuru-Guzik","year":"2018","venue":"ACS central science","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Gunantara, 2018] Nyoman Gunantara. A review of multi- objective optimization: Methods and its applications. Co- gent Engineering, 5(1):1502242, 2018.","title":"A review of multiobjective optimization: Methods and its applications","authors":"Nyoman Gunantara","year":"2018","venue":"Cogent Engineering","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Guo and Schwaller, 2024] Jeff Guo and Philippe Schwaller. Augmented memory: Sample-efficient generative molecu- lar design with reinforcement learning. JACS Au, 2024.","title":"Augmented memory: Sample-efficient generative molecular design with reinforcement learning","authors":"Jeff Guo, Philippe Schwaller","year":"2024","venue":"JACS Au","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Hager, 1979] William W Hager. Lipschitz continuity for constrained processes. SIAM Journal on Control and Op- timization, 17(3):321-338, 1979.","title":"Lipschitz continuity for constrained processes","authors":"William W Hager","year":"1979","venue":"SIAM Journal on Control and Optimization","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Huang et al., 2021] Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik. Ther- apeutics data commons: Machine learning datasets and tasks for drug discovery and development. arXiv preprint arXiv:2102.09548, 2021.","title":"Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development","authors":"Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W Coley, Cao Xiao, Jimeng Sun, Marinka Zitnik","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Hughes, 2005] Evan J Hughes. Evolutionary many- objective optimisation: many once or one many? In 2005 IEEE congress on evolutionary computation, volume 1, pages 222-227. IEEE, 2005.","title":"Evolutionary manyobjective optimisation: many once or one many?","authors":"Evan J Hughes","year":"2005","venue":"IEEE congress on evolutionary computation","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Jensen, 2019] Jan H Jensen. A graph-based genetic algo- rithm and generative model/monte carlo tree search for the exploration of chemical space. Chemical science, 10(12):3567-3572, 2019.","title":"A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration of chemical space","authors":"Jan H Jensen","year":"2019","venue":"Chemical science","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Jin et al., 2020] Wengong Jin, Regina Barzilay, and Tommi Jaakkola. Multi-objective molecule generation using in- terpretable substructures. In International conference on machine learning, pages 4849-4859. PMLR, 2020.","title":"Multi-objective molecule generation using interpretable substructures","authors":"Jin","year":"2020","venue":"International conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Laumanns and Ocenasek, 2002] Marco Laumanns and Jiri Ocenasek. Bayesian optimization algorithms for multi- objective optimization. In International Conference on Parallel Problem Solving from Nature, pages 298-307. Springer, 2002.","title":"Marco Laumanns and Jiri Ocenasek. Bayesian optimization algorithms for multiobjective optimization","authors":"Ocenasek Laumanns","year":"2002","venue":"International Conference on Parallel Problem Solving from Nature","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Lin et al., 2022] Xi Lin, Zhiyuan Yang, Xiaoyuan Zhang, and Qingfu Zhang. Pareto set learning for expensive multi- objective optimization. Advances in Neural Information Processing Systems, 35:19231-19247, 2022.","title":"Pareto set learning for expensive multiobjective optimization","authors":"Xi Lin, Zhiyuan Yang, Xiaoyuan Zhang, Qingfu Zhang","year":"2022","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Liu et al., 2021] Ruochen Liu, Ping Yang, and Jiangdi Liu. A dynamic multi-objective optimization evolutionary al- gorithm for complex environmental changes. Knowledge- Based Systems, 216:106612, 2021.","title":"A dynamic multi-objective optimization evolutionary algorithm for complex environmental changes","authors":"Liu","year":"2021","venue":"Knowledge-Based Systems","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Luukkonen et al., 2023] Sohvi Luukkonen, Helle W van den Maagdenberg, Michael TM Emmerich, and Gerard JP van Westen. Artificial intelligence in multi- objective drug design. Current Opinion in Structural Biology, 79:102537, 2023.","title":"Artificial intelligence in multiobjective drug design","authors":"Luukkonen","year":"2023","venue":"Current Opinion in Structural Biology","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Marler and Arora, 2004] R Timothy Marler and Jasbir S Arora. Survey of multi-objective optimization methods for engineering. Structural and multidisciplinary optimiza- tion, 26:369-395, 2004.","title":"Survey of multi-objective optimization methods for engineering","authors":"Arora ; R Timothy Marler, Jasbir S Marler, Arora","year":"2004","venue":"Structural and multidisciplinary optimization","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Mathur and Hoskins, 2017] Sunil Mathur and Clare Hoskins. Drug development: Lessons from nature. Biomedical reports, 6(6):612-614, 2017.","title":"Drug development: Lessons from nature","authors":"Hoskins Mathur, Sunil Mathur, Clare Hoskins","year":"2017","venue":"Biomedical reports","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Olivecrona et al., 2017] Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, and Hongming Chen. Molecular de-novo design through deep reinforcement learning. Journal of cheminformatics, 9(1):1-14, 2017.","title":"Molecular de-novo design through deep reinforcement learning","authors":"Olivecrona","year":"2017","venue":"Journal of cheminformatics","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Quan et al., 2022] Zhen Quan, Yan Wang, and Zhicheng Ji. Multi-objective optimization scheduling for manufactur- ing process based on virtual workflow models. Applied Soft Computing, 122:108786, 2022.","title":"Multi-objective optimization scheduling for manufacturing process based on virtual workflow models","authors":"Zhen Quan, Yan Wang, Zhicheng Ji","year":"2022","venue":"Applied Soft Computing","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Shin et al., 2024] Dong-Hee Shin, Young-Han Son, Jun-Mo Kim, Hee-Jun Ahn, Jun-Ho Seo, Chang-Hoon Ji, Ji-Wung Han, Byung-Jun Lee, Dong-Ok Won, and Tae-Eui Kam. MARS: Multiagent reinforcement learning for spatial- spectral and temporal feature selection in EEG-based BCI. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2024.","title":"MARS: Multiagent reinforcement learning for spatialspectral and temporal feature selection in EEG-based BCI","authors":"Dong-Hee Shin, Young-Han Son, Jun-Mo Kim, Hee-Jun Ahn, Jun-Ho Seo, Chang-Hoon Ji, Ji-Wung Han, Byung-Jun Lee, Dong-Ok Won, Tae-Eui Kam","year":"2024","venue":"IEEE Transactions on Systems, Man, and Cybernetics","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Son et al., 2024] Young-Han Son, Dong-Hee Shin, and Tae- Eui Kam. FTMMR: Fusion transformer for integrat- ing multiple molecular representations. IEEE Journal of Biomedical and Health Informatics, 2024.","title":"FTMMR: Fusion transformer for integrating multiple molecular representations","authors":"Son","year":"2024","venue":"IEEE Journal of Biomedical and Health Informatics","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Stanton et al., 2022] Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, and Andrew Gordon Wilson. Accelerating bayesian optimization for biological sequence design with denoising autoencoders. In International Conference on Machine Learning, pages 20459-20478. PMLR, 2022.","title":"Accelerating bayesian optimization for biological sequence design with denoising autoencoders","authors":"Stanton","year":"2022","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Sterling and Irwin, 2015] Teague Sterling and John J Irwin. Zinc 15-ligand discovery for everyone. Journal of chemi- cal information and modeling, 55(11):2324-2337, 2015.","title":"Zinc 15-ligand discovery for everyone","authors":"Irwin ; Sterling, Teague Sterling, John J Irwin","year":"2015","venue":"Journal of chemical information and modeling","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Sun et al., 2022] Mengying Sun, Jing Xing, Han Meng, Huijun Wang, Bin Chen, and Jiayu Zhou. Molsearch: search-based multi-objective molecular generation and property optimization. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pages 4724-4732, 2022.","title":"Molsearch: search-based multi-objective molecular generation and property optimization","authors":"Mengying Sun, Jing Xing, Han Meng, Huijun Wang, Bin Chen, Jiayu Zhou","year":"2022","venue":"Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Tripp et al., 2021] Austin Tripp, Gregor NC Simm, and José Miguel Hernández-Lobato. A fresh look at de novo molecular design benchmarks. In NeurIPS 2021 AI for Science Workshop, 2021.","title":"A fresh look at de novo molecular design benchmarks","authors":"Tripp","year":"2021","venue":"NeurIPS 2021 AI for Science Workshop","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Tseng and others, 1988] Paul Tseng et al. Coordinate as- cent for maximizing nondifferentiable concave functions. 1988. [Tušar and Filipič, 2014] Tea Tušar and Bogdan Filipič. Vi- sualization of pareto front approximations in evolutionary multiobjective optimization: A critical review and the pro- section method. IEEE Transactions on Evolutionary Com- putation, 19(2):225-245, 2014.","title":"Coordinate ascent for maximizing nondifferentiable concave functions. 1988. [Tušar and Filipič, 2014] Tea Tušar and Bogdan Filipič. Visualization of pareto front approximations in evolutionary multiobjective optimization: A critical review and the prosection method","authors":"Paul Tseng","year":"2014","venue":"IEEE Transactions on Evolutionary Computation","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Verhellen, 2022] Jonas Verhellen. Graph-based molecu- lar pareto optimisation. Chemical Science, 13(25):7526- 7535, 2022.","title":"Jonas Verhellen. Graph-based molecular pareto optimisation","authors":"Verhellen","year":"2022","venue":"Chemical Science","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Walters and Namchuk, 2003] W Patrick Walters and Mark Namchuk. Designing screens: how to make your hits a hit. Nature reviews Drug discovery, 2(4):259-266, 2003.","title":"Designing screens: how to make your hits a hit","authors":"Patrick Walters, Mark Namchuk","year":"2003","venue":"Nature reviews Drug discovery","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Wang et al., 2006] Mingliang Wang, Xiangqian Hu, David N Beratan, and Weitao Yang. Designing molecules by optimizing potentials. Journal of the American Chemical Society, 128(10):3228-3232, 2006.","title":"Designing molecules by optimizing potentials","authors":"Mingliang Wang, Xiangqian Hu, David N Beratan, Weitao Yang","year":"2006","venue":"Journal of the American Chemical Society","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Xie et al., 2021] Yutong Xie, Chence Shi, Hao Zhou, Yuwei Yang, Weinan Zhang, Yong Yu, and Lei Li. Mars: Markov molecular sampling for multi-objective drug discovery. arXiv preprint arXiv:2103.10432, 2021.","title":"Mars: Markov molecular sampling for multi-objective drug discovery","authors":"Yutong Xie, Chence Shi, Hao Zhou, Yuwei Yang, Weinan Zhang, Yong Yu, Lei Li","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Yuan et al., 2015] Yuan Yuan, Hua Xu, Bo Wang, Bo Zhang, and Xin Yao. Balancing convergence and diversity in decomposition-based many-objective optimiz- ers. IEEE Transactions on Evolutionary Computation, 20(2):180-198, 2015.","title":"Balancing convergence and diversity in decomposition-based many-objective optimizers","authors":"Yuan Yuan, Hua Xu, Bo Wang, Bo Zhang, Xin Yao","year":"2015","venue":"IEEE Transactions on Evolutionary Computation","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Zhang and Li, 2007] Qingfu Zhang and Hui Li. MOEA/D: A multiobjective evolutionary algorithm based on decom- position. IEEE Transactions on evolutionary computation, 11(6):712-731, 2007.","title":"MOEA/D: A multiobjective evolutionary algorithm based on decomposition","authors":"Li ; Zhang, Qingfu Zhang, Hui Li","year":"2007","venue":"IEEE Transactions on evolutionary computation","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Zhu et al., 2023] Yiheng Zhu, Jialu Wu, Chaowen Hu, Ji- ahuan Yan, Chang-Yu Hsieh, Tingjun Hou, and Jian Wu. Sample-efficient multi-objective molecular optimization with GFlownets. In Thirty-seventh Conference on Neural Information Processing Systems, 2023.","title":"Sample-efficient multi-objective molecular optimization with GFlownets","authors":"Yiheng Zhu, Jialu Wu, Chaowen Hu, Jiahuan Yan, Chang-Yu Hsieh, Tingjun Hou, Jian Wu","year":"2023","venue":"Thirty-seventh Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Zitzler et al., 2003] Eckart Zitzler, Lothar Thiele, Marco Laumanns, Carlos M Fonseca, and Viviane Grunert Da Fonseca. Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transactions on evolutionary computation, 7(2):117-132, 2003.","title":"Performance assessment of multiobjective optimizers: An analysis and review","authors":"Eckart Zitzler, Lothar Thiele, Marco Laumanns, Carlos M Fonseca, Viviane Grunert, Da Fonseca","year":"2003","venue":"IEEE Transactions on evolutionary computation","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}