** Apologies for multiple posting ** ***************************************************************************** *** CALL FOR PAPERS *** *** 2014 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO-2014) *** *** Estimation of Distribution Algorithms (EDAs) *** *** July 12-16, 2014, Vancouver, BC, Canada *** *** Organized by ACM SIGEVO *** *** http://www.sigevo.org/gecco-2014 *** *****************************************************************************
Description: ------------ Estimation of distribution algorithms (EDAs) are based on the explicit use of probability distributions. They replace traditional variation operators of evolutionary algorithms, such as mutation and crossover, by building a probabilistic model of promising solutions and sampling the built model to generate new candidate solutions. Using probabilistic models for exploration in evolutionary algorithms enables the use of advanced methods of machine learning and statistics for automated identification and exploitation of problem regularities for broad classes of problems. In addition they provide natural ways to introduce problem information into the search by means of the probabilistic model and also to get information about the problem that is being optimized. EDAs provide a robust and scalable solution to many important classes of optimization problems with only little problem specific knowledge. The aim of the track is to attract the latest high quality research on EDAs in particular, and the use of explicit probabilistic and graphical models in evolutionary algorithms in general. We encourage the submission of original and previously unpublished work especially in the following areas: 1. Advances in the theoretical foundations of EDAs. 2. Position papers on EDA-related topics. 3. Reviews of specific EDA-related aspects. 4. Statistical and machine learning modeling in evolutionary algorithms. 5. EDAs for dynamic, multiobjective or noisy problems and 6. Interactive and self-adaptive EDAs. 7. EDAs in practical decision making. 8. Links between EDAs and other model-based search. 9. Large scale EDAs. 10. EDAs based on new algorithms for learning probabilistic models from data. 11. Probabilistic models as fitness surrogates in evolutionary algorithms. 12. Interfaces between EDA and Ant Colony Optimization, Evolution Strategies, Cross-Entropy Method or other related methods. 13. Comparisons of EDAs and other metaheuristics, evolutionary algorithms, more traditional optimization methods of operations research or hybrids thereof. 14. Hybridizing EDAs with other metaheuristics. 15. Novel applications for EDAs. The above list of topics is not exhaustive; if you think that your work does not fit the above categories but the work should belong to the EDA track, please contact the track chairs to discuss this issue. All accepted papers will appear in the proceedings of GECCO 2014, which will be published by ACM (Association for Computing Machinery). Important Dates: ---------------- * Abstract submission: January 15, 2014 * Submission of full papers: January 29, 2014 * Notification of paper acceptance: March 12, 2014 * Camera ready submission: April 14, 2014 * Advance registration: May 2, 2014 * Conference: July 12-16, 2014 Track Chairs: ------------- - John McCall, [email protected] - Pedro Larranaga, [email protected] Robert Gordon University is The Sunday Times Best Modern University in the UK 2012 Robert Gordon University, a Scottish charity registered under charity number SC 013781. This e-mail and any attachment is for authorised use by the intended recipient(s) only. It may contain proprietary material, confidential information and/or be subject to legal privilege. It should not be copied, disclosed to, retained or used by, any other party. If you are not an intended recipient then please promptly delete this e-mail and any attachment and all copies and inform the sender. Please note that any views or opinions presented in this email are solely those of the author and do not necessarily represent those of Robert Gordon University. Thank you. _______________________________________________ uai mailing list [email protected] https://secure.engr.oregonstate.edu/mailman/listinfo/uai
