Optimization Group Meetings
You can subscribe to an iCalendar of these meetings at http://iridia.ulb.ac.be/Calendars/publish.php/_NUC_Optimization.ics
Breakfast meetings are informal meetings where there is a general discussion or someone presents their work. And additional goal is to have breakfast together.
- Send an email to the responsible of Optimization Meetings. Mention your name, your affiliation, the date, time and room, and a short summary of what you are going to talk about.
- Add a new entry to the table below.
- Bring breakfast.
History of Breakfast Meetings
|2010-12-21||Paola Pellegrini||Off-line vs. On-line Tuning: A Study on MAX-MIN Ant System for the TSP|
|2010-10-19||Stefano Benedettini||Experiments on Boolean Network Design by Local Search Algorithms.|
|2010-05-20||Marco Montes de Oca||The social learning strategies tournament, organized as part of the cultaptation project, and its results were described. No ideas about algorithm portfolios were discussed.|
|2010-04-29||Franco Mascia||The maximum clique problem, state of the art and ACO   |
|2010-03-18||Sabrina Oliveira||Achieved results from the application of the Population Based Ant Colony Optimisation algorithm to TSP.|
|2010-02-11||Sara Ceschia||An overview of Container Loading Problems.|
|2010-01-21||Yuan, Zhi (Eric)||Rice cooker vs. the idea of tuning.|
|2010-01-07||Thomas StÃ¼ztle||General discussion about the optimization group.|
The goal of the Literature Sessions is to examine and discuss particularly interesting papers from the research literature. For each session, one paper will be selected, there will be a short presentation (10-15 minutes) about the contents, and a discussion will ensue. Sessions will last around 40-45 minutes. Attendants should read the paper before the session in order to have a productive discussion.
- Book a room (ask Muriel).
- Send an email to the responsible of Optimization Meetings. Mention your name, your affiliation, the date, time and room, a reference to the paper and the URL where it can be obtained. Also, attach the paper in PDF.
- Add a new entry to the table below. Use YYY-MM-DD (year-month-day) format for the date. Please add a complete bibliographic reference (you may find it in the homepages of the authors) and a hyperlink to a PDF (links to http://dx.doi.org are preferred).
History of Literature Sessions
|2010-12-17||Marco Montes de Oca||A. LaTorre, S. Muelas, and J.-M. PeÃ±a. A MOS-based dynamic memetic differential evolution algorithm for continuous optimization: a scalability test. Soft Computing. Special issue on Scalability of Evolutionary Algorithms and other Metaheuristics for Large Scale Continuous Optimization Problems. DOI 10.1007/s00500-010-0646-3|
|2010-11-10||Manuel LÃ³pez-IbÃ¡Ã±ez||A Rigorous Analysis of the Harmony Search Algorithm--How the Research Community can be misled by a "novel" Methodology. Dennis Weyland. International Journal of Applied Metaheuristic Computing, volume 1-2, April-June 2010, pages 50-60. DOI: 10.4018/jamc.2010040104
Survival of the Fittest Algorithm or the Novelest Algorithm?: The Existence Reason of the Harmony Search Algorithm. Zong Woo Geem.
|2010-09-23||Gianpiero Francesca||Wenyin Gong, Ãlvaro Fialho and Zhihua Cai. Adaptive Strategy Selection in Differential Evolution. In J. Branke et al., eds.: "GECCO'10: Proc. 12th Annual Conference on Genetic and Evolutionary Computation", ACM Press : p. 409-416. July 2010. 
Ãlvaro Fialho, Marc Schoenauer and Michele Sebag. Toward Comparison-based Adaptive Operator Selection. In J. Branke et al., eds.: "GECCO'10: Proc. 12th Annual Conference on Genetic and Evolutionary Computation", ACM Press : p. 767-774. July 2010. 
|2010-06-03||Tianjun Liao||Population-Based Algorithm Portfolios for Numerical Optimization. Peng, F. Tang, K.Chen, G.Yao, X. IEEE Transactions on Evolutionary Computation. DOI: 10.1109/TEVC.2010.2040183|
|2010-05-07||Zhi (Eric) Yuan||Hydra: Automatically Configuring Algorithms for Portfolio-Based Selection. L. Xu, H.H. Hoos, K. Leyton-Brown. To appear at the Conference of the Association for the Advancement of Artificial Intelligence (AAAI-10), 2010. |
|2010-03-04||Sara Ceschia||A. Schaerf and L. Di Gaspero. Slides of the tutorial "An Overview of Local Search Software Tools" given at "Learning and Intelligent OptimizatioN (LION 2007)", December 8-12, 2007, Trento, Italy.
L. Di Gaspero and A. Schaerf. EASY LOCAL++: An object-oriented framework for ï¬‚exible design of local search algorithms. Software â€” Practice & Experience, 33(8):733â€“765, July 2003. 
S. Cahon, N. Melab and T. El Ghazali. ParadisEO: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics. Journal of Heuristics, 10(3):357-380, November 2004. 
|2010-02-19||Sabrina M. de Oliveira||A study on the use of non-parametric tests for analyzing the evolutionary algorithms' behaviour: a case study on the CEC'2005 Session on Real Parameter Optimization. S. Garcia, D. Molina, M. Lozano, F. Herrera - Journal of Heuristics, Volume 15, pp. 617-644, 2009. |
|2010-02-04||Thomas StÃ¼ztle||Analyzing Bandit-based Adaptive Operator Selection Mechanisms. Ãlvaro Fialho, Luis Da Costa, Marc Schoenauer and MichÃ©le Sebag.|
|2010-01-14||JÃ©rÃ©mie Dubois-Lacoste||SATzilla: Portfolio-based Algorithm Selection for SAT. L. Xu, F. Hutter, H. H. Hoos, K. Leyton-Brown - Journal of Artificial Intelligence Research, Volume 32, pp. 565-606, 2008. |
|2009-12-11||Manuel LÃ³pez-IbÃ¡Ã±ez||SATenstein: Automatically Building Local Search SAT Solvers From Components. Ashiqur R. KhudaBukhsh, Lin Xu, Holger H. Hoos and Kevin Leyton-Brown - Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI-09), pp. 517-524, 2009. |