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10 years of news and resources for members of the IEEE Signal Processing Society
Most evolutionary algorithms and other meta-heuristic search methods typically assume that there are explicit objective functions available for fitness evaluations. In the real world, such explicit objective functions may not exist in many cases. Instead, computationally very intensive numerical simulations, such as computational fluid dynamic simulations or finite element analysis, must be conducted. In many process industry optimization problems, no analytical mathematical models exist for describing the functional relationship between the final quality of the product and the decision variables, such as temperature and humidity. Thus, only historical experimental data can be used for optimization. There are also cases where only factual data can be collected.
The special issue on Computational Intelligence in Data-Driven Optimization published in IEEE Transactions on Emerging Topics in Computational Intelligence in April 2019 in aims to present the most recent advances in the use of computational intelligence, in particular methodologies integrating evolutionary algorithms and other metaheuristic search methods with machine learning and neural networks, for on-line or off-line data-driven real-world optimization problems. Following a rigorous peer review process, seven papers have been accepted to be included in the special issue.
|Call for Nominations for Editor-in-Chief: IEEE Open Journal of Signal Processing||1 August 2019|
|Nominations Open for 2019 SPS Awards||1 September 2019|
|Call for Nominations: Technical Committee Vice Chair and Member Positions||15 September 2019|
|Call for Nominations: Awards Board and Nominations and Appointments Committee||27 September 2019|
|Election of President-Elect, Regional Directors-at-Large and Members-at-Large||1 October 2019|
|Call for Nominations: SPS Chapter of the Year Award||15 October 2019|
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