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Nowadays, in all applied fields, there exists a wide range of challenges that need the development of techniques with solid mathematical foundations, in order to give answers to different problems. Many of these problems require advanced mathematical techniques, among which we can mention fuzzy techniques capable of dealing with imprecise, uncertain or incomplete information or methods based on other branches of computational intelligence (CI), such as various kinds of connectionist systems, and algorithms that copy the evolution of biological species, or the behavior of whole populations, and which often help solve “unsolvable” (intractable) mathematical problems in the form of high quality and efficient approximation. A special class of these methods is the group of memetic algorithms, which always combine CI in the form of the latter class of methods with the classic optimization techniques developed by classical mathematics, such as gradientbased techniques, or local subgraph optimization. Combining the advanced computer science techniques provided by CI with mathematical foundations makes them more efficient, robust and solid. As a consequence, the results of those processes in which they are applied are improved, compared to both purely mathematical and purely CImethods, this waymaking themmore efficient, more reliable and much wider applicable.

Mathematics and Computational Intelligence Synergies for Emerging Challenges