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
|2011-03-10||Stefanie Kritzinger||A Unified Framework for Routing Problems with Fixed Fleet Size|
|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
|2012-04-19||Franco Mascia||E.K. Burke, M.R. Hyde, and G. Kendall. Grammatical Evolution of Local Search Heuristics. IEEE Transactions on Evolutionary Computation, 99, 2011. doi: 10.1109/TEVC.2011.2160401 PDF|
|2012-04-03||JÃ©rÃ©mie Dubois-Lacoste||On the automatic discovery of variants of the NEH procedure for flow shop scheduling using genetic programming. J A VÃ¡zquez-RodrÃguez and G Ochoa.Journal of the Operational Research Society (2011) 62, 381â€“396. doi:10.1057/jors.2010.132|
|2011-12-08||Zhi Yuan||Frank Hutter, Holger Hoos, and Kevin Leyton-Brown. Sequential Model-Based Optimization for General Algorithm Configuration. In Proceedings of Learning and Intelligent Optimization (LION 5), 2011. PDF: |
|2011-11-24||Manuel LÃ³pez-IbÃ¡Ã±ez||Ender Ã–zcan and Andrew J. Parkes. 2011. Policy matrix evolution for generation of heuristics. In Proceedings of the 13th annual conference on Genetic and evolutionary computation (GECCO 2011), Natalio Krasnogor (Ed.). ACM, New York, NY, USA, 2011-2018. DOI: 10.1145/2001576.2001846|
|2011-02-04||Paola Pellegrini||HAL: A Framework for the Automated Analysis and Design of High-Performance Algorithms. By Christopher Nell, Chris Fawcett, Holger H. Hoos, and Kevin Leyton-Brown. PDF|
|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. |