Recent years have seen many new developments in computational intelligence (CI) techniques and, consequently, led to an exponential increase in the number of applications in a variety of areas, including: engineering, computer science, finance, and biomedical. Although this discipline is well-established, recent research shows that new algorithms, and the beauty of the subject, attracts every year newcomers to investigate in this area. The next steps are undoubtedly in discovering new paths to shaping these models with better explanation tools which might be able to communicate and transfer knowledge to the scientists in all areas.
This summary is not intended to be a in-dept analysis of all the problems behind computational learning models nor a broad coverage of the topic. It lays down the author’s thought on many years of experience dealing with many problems of scientific interest.