Aim and scope:High fidelity CAE models result in very detailed and precise information on the system at hand and offer a huge potential for the utilization of numerical optimization methods like evolutionary computation. However a big challenge is the resulting high computational cost of the models which often even hinders the application of evolutionary optimization and design methods. In the literature various methods are proposed to tackle the problem like for example approximation methods or surrogates which are used to reduce computationally expensive optimization problems. However this involves many challenges in tailoring the approximation/surrogate to each problem in question. The aim of this session is to bring together researchers from evolutionary algorithms who deal with computationally expensive problems. This is an ideal platform for researchers and practitioners to interact and present ideas for handling computationally expensive problems.
List of main topics:The topics include (but not limited to):
- Reducing of function evaluations using function, fitness, problem approximation and multi-level approaches
- Surrogate based methods in optimization
- Preference based methods including preference learning and many objective optimization
- Parallelization/grid/cloud of evolutionary algorithms
- Data-driven optimization using big data and data analytics
- Real world applications