New methodologies to computational intelligence instigate from the initiative that intelligence emerges as much from cells, substances, and societies as it does from evolution, development, and learning. Conventionally, computational intelligence has been concerned with reproducing the abilities of human brains; newer approaches take inspiration from a wider range of biological systems that are capable of autonomous self-organization. Examples (to list a few) of these new approaches include evolutionary computation, swarm intelligence, artificial neural networks, artificial immune systems, and emergent systems.

Biological systems exhibit remarkably effective problem-solving strategies in complex, dynamic, and uncertain situations. They are particularly suitable to deal with complicated, ill-defined, dynamic systems which are not acquiescent of traditional solution methods. Rather, results and lessons learnt from the application of biologically-inspired computational intelligence to real-world problems are of major significance for methodological research with the intention of refining the existing methods or devising new techniques.

The overall aim of this special issue is to bring together the latest/innovative advancements and modeling of biologically-inspired computational intelligence techniques for handling complex problems in optimization. This special issue also provides a forum for researchers and practitioners to review and disseminate quality research work on computational intelligence techniques with emphasis on the mathematical modeling and analysis of algorithms and their applications in the context of science and engineering and to identify critical issues for further developments.

Potential topics include, but are not limited to:

Latest theoretical and technological advancements in mathematical modeling for biologically inspired computational intelligence algorithms based on biological learning mechanisms, with validation through convincing computational experiments, comparison, and convergence proof
Architectural design and development of new approaches combining neural networks, evolutionary computation, and other recent computational intelligence methods in the bio-inspired paradigm with strong mathematical proof
Application of biologically-inspired computational intelligence algorithms for complex optimization problems in medical science, neuroscience, and engineering science. Problems related to the mathematical design, evaluation, and analysis of biologically inspired algorithms in large scale optimization problems and other problems in real-world applications
Authors can submit their manuscripts via the Manuscript Tracking System at http://mts.hindawi.com/submit/journals/mpe/icat/ or Mathematical Problems in Engineering.




Lead Guest Editor
Albert Victoire, Institut National des Sciences Appliquées de Lyon, Lyon, France

Guest Editors
Bo-Wei Chen, Princeton University, Princeton, USA
Sishaj P. Simon, National Institute of Technology, Tiruchirappalli, India
S. N. Deepa, Anna University, Chennai, India