Aim: As the complexity of engineering and science projects continues to increase, classical solution methodologies often tend to perform poorly in such settings. This has motivated the development and application of computational intelligence methods in such scenarios. Examples include nature-inspired optimization approaches, neural network based approximations in simulation-driven problems, machine learning techniques to enhance the engineering process, and agent-based simulations of complex systems, to name a few.
The goal of this symposium is to present some of the recent advances in the field of applied computational intelligence, and to provide a vivid forum for discussion both on theoretical and practical aspects of this discipline.

Topics: Contributions related, but not limited, to the following topics of interest are welcome. Models, methods, algorithms, computational techniques and applications in:
- Dedicated implementations of computational intelligence methods (such as nature inspired optimization, neural networks, and fuzzy logic).
- Reduction of expensive evaluations (surrogates, variable-fidelity, fitness inheritance).
- Methods for multiobjective, constrained, or robust problems.
- Parallelization of algorithms (implementations on clusters, grids, parallel machines).
- Incorporation of expert systems and human-system interface.
- Data mining and statistical analysis as assistive tools.
- Examples and analysis of real-world applications in science and engineering.