Computational Intelligence

Dynamics of evolution and adaptation have been obviously discussed in the biology context, since the emergence of Charles Darwin’s evolutionary theory. Nowadays, inspired by biological and neurological processes, evolution and adaptation are often considered to be mechanisms that can explain biological processes and create non-biological processes that have dynamic learning and creative abilities. These processes are often discussed in the complexity science context involving:

• evolutionary computation
• mathematical programming/optimization
• artificial neural networks
• and another frameworks of man-made adaptive systems as machine learning.

The analysis of the growing wave of data has become important for a wide range of fields including humanities, medicine and business, as well as engineering and science. It is increasingly urgent for organizations that want to use big data to improve their strategies, to discover new insights and to optimize their decision-making, to develop methods and algorithms that support decision-making and to give it the highest possible rigor.

Machine learning, Queues model, bio-inspired algorithms, optimality conditions, multi criteria decision, goal programming