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Acronym Meta-X
Name Meta-X: Metaheuristics for Complex Optimization Problems
Description The research topic of "Meta-X: Metaheuristics for Complex Optimization Problems" is the study of metaheuristics for the solution of difficult problems in discrete optimization. Metaheuristics are a family of optimization techniques, which have seen increasingly rapid development over the past few years. Well known examples are Simulated Annealing, Evolutionary computation or Ant Colony Optimization, a metaheuristic that has been developed at the CoDE-IRIDIA laboratory. Metaheuristics are applicable to numerous problems in science, industry and commerce, including scheduling, distribution management, bioinformatics, telecommunications, and many others.

Meta-X is a collaborative project between the CoDE-IRIDIA and CoDE-SMG laboratories. In Meta-X we will tackle problems that have multiple objectives, dynamically changing characteristics or data, or where some information is stochastic, and combinations of such features. These features arise commonly in real-world applications and accounting for them properly would lead to substantial economic, environmental and scientific gains; yet, they have been largely overlooked in existing research. We believe the field of metaheuristics has now reached a level of maturity that makes it a realistic prospect, for the first time, that these pressing and crucial features of real-world problems can be tackled.
Persons P. Balaprakash, M. S. bin Hussin, M. Birattari, Y. De Smet, M. Dorigo, J. Dubois-Lacoste, S. Eppe, E. Ferrante, R. Hendrickx, T. Liao, M. López-Ibáñez, T. Stützle, Z. Yuan
Funding Scientific Research Directorate of the French Community of Belgium
Dates 2008-10-01 to 2013-09-30

Publications

List of publications of the project:


Updated: 2017-02-03 11:50:44 GMT