Massively complex problems are prevalent nowadays. Therein, complexity is to be understood as the intricate relationship among the components of the corresponding system (be it a computational environment or a system under scrutiny) giving rise to emergent properties of the latter, as well as the sheer difficulty for coping with the system due to its size or its dynamic nature, just to mention a couple of features. This is very well exemplified in emergent computational settings such as volunteer computing and P2P networks, and in massively large data environments. Indeed, in the age of Big Data, new methods and algorithms for properly managing heterogeneous computing resources and large collections of data are required. In light of this context, algorithms used have to be flexible, resilient and self-adaptive. Bioinspired algorithms fit nicely here, since they natively incorporate these features or can be readily augmented with them. In this sense, we can think of deep bioinspired algorithms exhibiting multiple interconnected layers contributing the desired characteristics by encapsulating the tools required to tackle the different aspects of the complexity of the problem and the intricacy of the computational substrate.

Topics of Interest

This session welcomes research on deep bioinspired algorithms. Techniques of interest include (but are not limited to):

• Memetic algorithms
• Hybrid bioinspired algorithms
• Surrogate-based bioinspired optimization
• Co-evolutionary and meta-evolutionary techniques
• Unplugged bioinspired algorithms
• Hierarchical bioinspired algorithms
• …

Relevant topics regarding these techniques include (but are not limited to):

• Big Data applications
• Dynamic optimization
• Industrial/Educational/Corporative applications
• Self-* properties
• Deployment on irregular computational environment
• Creativity and user-centric approaches
• Sustainable computing
• …

Organizers

Carlos Cotta, Universidad de Málaga, Spain
Francisco Fernández de Vega, Universidad de Extremadura, Spain