Dynamic multi-objective optimization problems (DMOPs) involve multiple conflicting and time-varying objectives, which widely exist in real-world problems. Evolutionary algorithms are broadly applied to solve DMOPs due to their competent ability in handling highly complex and non-linear problems and most importantly solving those problems that cannot be addressed by traditional optimization methods.
Without generality, the minimization problem is considered here and a DMOP [1] can be mathematically formulated as follows:
Evolutionary algorithms for solving DMOPs are called dynamic multi-objective evolutionary algorithms (DMOEAs) [2]. In the development of DMO, a mature framework of DMOEAs [3] has been proposed by researchers, as shown in Fig. 1.
The detailed steps of the framework of DMOEAs are as follows:
Most existing work in DMO mainly focus on how to improve the effectiveness of response mechanisms.
[1]. M. Farina, K. Deb, P. Amato, Dynamic multiobjective optimization problems: test cases, approximations, and applications, IEEE Trans. Evolut. Comput. 8 (5) (2004) 425–442.
[2]. S. Yang and X. Yao, Evolutionary Computation for Dynamic Optimization Problems. Springer, 2013, vol. 490.
[3]. G. Ruan, G. Yu, J. Zheng, J. Zou, and S. Yang, “The effect of diversity maintenance on prediction in dynamic multi-objective optimization,” Applied Soft Computing, vol. 58, pp. 631–647, 2017.
Time flies fast. It has been almost over one year since I joined ECOLE. And it has been an exciting year. Travelling between Birmingham, Warsaw, Leiden, Berlin, Coimbra and Xiamen. Listening to talks in state of the art on research in Evolutionary Computation and Machine Learning. Attending workshops in the research institutions of two world-known tech companies. The opportunities promised and given by the Innovative Training Network have not fallen short. But aside from all the excitement and activities, who am I, what do I do and what is that drove me to ECOLE in the first place?
Well, my prior academic pursuits were in Theoretical Nuclear Physics at the Technical University Darmstadt. And while I of course enjoyed studying the field and gaining understanding into the inner subnuclear workings of the world, I felt at times the research self was missing out in some way on my initial fascinations which took me too it. So, where to go else? Stumbling upon ECOLE was to be honest a bit of a lucky coincidence. I was working prior at software company while looking for PhD and residency programs, when a friend of mine was recommending me the posting for an Early Stage Researcher position he found at one of the partner institutions. I applied, was interviewed and subsequentially have been offered the position. It has been a bit unusual for me to travel this much for the Marie Curie Fellowship, but I am happy to have found a new home. And with Computational Intelligence research being situated at the intersection between Natural Computation and Artificial Intelligence, there is still plenty of space for me to philosophize about nature, while keeping in touch with reality through real-world problems.
ECOLE is especially great in regards of opportunities to learn, grow and try out new things. I particularly had a lot of fun (but also stress) with preparing and giving a short keynote-style public talk on nature-inspired artificial intelligence for the popular science segment of the Z2X festival in Berlin last August. The talk was luckily to be held lightweight and in front of a small audience. I decided to talk about the history and inspirations behind current popular methods and highlight evolutionary approaches as a means for creative problem-solving. While part of the audience seemed to have got lost with some of the bare technicalities, the main ideas sticked. Which was the most important thing for me. I was thus more than happy to have received and interesting questions in return. By the way: I am planning to put my practice slides online soon, if you are interested, feel free to take a look. The actual talk was given completely in oral form, as I was informed one day prior that the beamer was missing, but it worked out.
So, what’s up for the future? Currently, I work on model-based approaches as a means to tackle the transfer learning problem in continuous optimisation. A bit of a complicated idea, but I find this notion to be intuive and interesting to explore. In any way, I will keep you updated about my work in a future blog post. Until then, everyone keep up with the good work! 🙂
The adaptive synthetic (ADASYN) sampling technique is a method that aims to adaptively generate minority samples according to their distributions [5]. The main improvement compared to SMOTE is the samples which are harder to learn are given higher importance and will be oversampled more often in ADASYN. The general idea of ADASYN is shown in Figure 3.
[1]. He, H. and Garcia, E.A., 2009. Learning from imbalanced data. IEEE Transactions on knowledge and data engineering, 21(9), pp.1263-1284
[2]. Ganganwar, V., 2012. An overview of classification algorithms for imbalanced datasets. International Journal of Emerging Technology and Advanced Engineering, 2(4), pp.42-47.
[3]. Santos, M.S., Soares, J.P., Abreu, P.H., Araujo, H. and Santos, J., 2018. Cross-validation for imbalanced datasets: Avoiding overoptimistic and overfitting approaches [research frontier]. ieee ComputatioNal iNtelligeNCe magaziNe, 13(4), pp.59-76.
[4]. Chawla, N.V., Bowyer, K.W., Hall, L.O. and Kegelmeyer, W.P., 2002. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16, pp.321-357.
[5]. He, H., Bai, Y., Garcia, E.A. and Li, S., 2008, June. ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence) (pp. 1322-1328). IEEE.
[1] Lütkepohl, Helmut. New introduction to multiple time series analysis. Springer Science & Business Media, 2005.
[2] LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. “Deep learning.” nature 521.7553 (2015): 436-444.
[3] Williams, Ronald J., and David Zipser. “A learning algorithm for continually running fully recurrent neural networks.” Neural computation 1.2 (1989): 270-280.
[4] Rangapuram, Syama Sundar, et al. “Deep state space models for time series forecasting.” Advances in Neural Information Processing Systems. 2018.
[5] Qiu, Jinwen, S. Rao Jammalamadaka, and Ning Ning. “Multivariate Bayesian Structural Time Series Model.” Journal of Machine Learning Research 19.68 (2018): 1-33.
[6] Harutyunyan, Hrayr, et al. “Multitask learning and benchmarking with clinical time series data.” arXiv preprint arXiv:1703.07771 (2017).
[7] Guo, Tian, et al. “Robust online time series prediction with recurrent neural networks.” 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA). Ieee, 2016.
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