An Interactive Decision Tree-Based Evolutionary Multi-objective Algorithm

Author:

Shavarani Seyed MahdiORCID,López-Ibáñez ManuelORCID,Allmendinger RichardORCID,Knowles JoshuaORCID

Publisher

Springer Nature Switzerland

Reference31 articles.

1. Afsar, B., Miettinen, K., Ruiz, F.: Assessing the performance of interactive multiobjective optimization methods: a survey. ACM Computing Surveys 54(4) (2021). https://doi.org/10.1145/3448301

2. Allen, J., Moussa, A., Liu, X.: Human-in-the-loop learning of qualitative preference models. In: Barták, R., Brawner, K.W. (eds.) Proceedings of the Thirty-Second International Florida Artificial Intelligence Research Society Conference, pp. 108–111. AAAI Press (2019)

3. Battiti, R., Passerini, A.: Brain-computer evolutionary multiobjective optimization: a genetic algorithm adapting to the decision maker. IEEE Trans. Evol. Comput. 14(5), 671–687 (2010). https://doi.org/10.1109/TEVC.2010.2058118

4. Biscani, F., Izzo, D., Yam, C.H.: A global optimisation toolbox for massively parallel engineering optimisation. Arxiv preprint arXiv:1004.3824 (2010). http://arxiv.org/abs/1004.3824

5. Branke, J., Deb, K., Miettinen, K., Słowiński, R. (eds.): Multiobjective Optimization: Interactive and Evolutionary Approaches. Lecture Notes in Computer Science, vol. 5252. Springer, Heidelberg (2008)

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