M2 AIC: Advanced Optimization

Details on the Paper Project (aka "contrôle continu" aka Final Exam)

Since the aim of the "Advanced Optimization" lecture is to prepare for possible Master's and PhD theses on the topic of (blackbox) optimization, the lecture will be accompanied by a small paper project in which each student reads, understands, and presents a scientific paper. An oral presentation together with a short oral exam will replace the classical final exam.

Rules and Deadlines

• maximal 3 students per paper
• paper assignments made until December 6, 2016 (3rd lecture)
• summarize the paper in a short abstract in your own words (4000 characters max.)
• hand the abstract in via the web form until January 9, 2017, 23h59
• individually present the paper orally after the end of the course (15min presentation + 15min oral "exam")
• oral presentation dates to be decided (students will receive a separate email with potential dates)
• slides to be sent by email to Anne Auger and Dimo Brockhoff until last lecture (Jan. 31, 2017)

List of Papers

Note that all papers are relevant to current research in the Randopt team. Papers with stars indicate possible concrete research projects as follow-ups.

• 1*) D. Brockhoff, M. Lopez-Ibanez, B. Naujoks, and G. Rudolph (2012). Runtime Analysis of Simple Interactive Evolutionary Biobjective Optimization Algorithms. In Parallel Problem Solving from Nature (PPSN 2012), pages 123-132. Springer. (paper)
• 2*) K. Bringmann, T. Friedrich, and P. Klitzke (2014). Two-dimensional subset selection for hypervolume and epsilon-indicator. In Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation (GECCO 2014), pages 589-596. ACM. (paper)
• 3*) Q. Zhang, A. Zhou, and Y. Jin (2008). RM-MEDA: A regularity model-based multiobjective estimation of distribution algorithm. IEEE Transactions on Evolutionary Computation, 12(1), pages 41-63. (paper)
• 4*) H. Lin, J. Mairal, and Z. Harchaoui (2015). A universal catalyst for first-order optimization. In Advances in Neural Information Processing Systems (NIPS 2015), pages 3384-3392. (paper)
• 5*) T. Glasmachers (2014). Optimized Approximation Sets for Low-Dimensional Benchmark Pareto Fronts. In International Conference on Parallel Problem Solving from Nature (PPSN 2014), pages 569-578. Springer. (paper)
• 101) M. Wagner, K. Bringmann, T. Friedrich, and F. Neumann (2015). Efficient optimization of many objectives by approximation-guided evolution. European Journal of Operational Research 243 (2), pages 465-479. (paper)
• 102) C. Igel, N. Hansen, and S. Roth (2007). Covariance matrix adaptation for multi-objective optimization. Evolutionary computation, 15(1), pages 1-28. MIT Press. (paper)
• 103) S. Bleuler. M. Laumanns, L. Thiele, and E. Zitzler (2003). PISA --- A Platform and Programming Language Independent Interface for Search Algorithms. In Evolutionary Multi-Criterion Optimization (EMO 2003), pages 494-508. LNCS, Springer. (paper)
• 104) I. Erlich, G. K. Venayagamoorthy, and W. Nakawiro (2010). A Mean-Variance Optimization Algorithm. In IEEE World Congress on Computational Intelligence (WCCI 2010), Barcelona, Spain. (paper)
• 105) Y. Akimoto, Y. Nagata, I. Ono, adn S. Kobayashi (2012). Theoretical foundation for CMA-ES from information geometry perspective. Algorithmica, 64(4), pages 698-716. (paper)
• 106) K. Nishida and Y. Akimoto (2016). Population Size Adaptation for the CMA-ES Based on the Estimation Accuracy of the Natural Gradient. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2016), ACM. (paper)
• 107) O. Krause, D. R. Arbones, and C. Igel (2016). CMA-ES with Optimal Covariance Update and Storage Complexity. In Advances In Neural Information Processing Systems (NIPS 2016), pages 370-378. (paper)
• 108) T. Glasmachers, T. Schaul, S. Yi, D. Wierstra, and J. Schmidhuber (2010). Exponential natural evolution strategies. In Proceedings of the 12th annual conference on Genetic and evolutionary computation (GECCO 2010), pages 393-400. ACM. (paper)

Last updated: Thu, 01 Jan 1970 01:00