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Bioinformatics, Biomedecine and Neuroscience represent a great challenge for optimization methods as many problems arizing in these fields can be modelized as large size optimization problems. For example, many bioinformatics problems deal with the manipulation of large sets of variables (SNPs, genes, GWA, proteins ...). Hence, looking for a good combination of these variables require advance search mechanisms. In biomedecine (or medical biology), such optimization problems may also be found by studying molecular interactions. Solving such difficult combinatorial optimization problems require to incorporate knowledge about problems to be solved.

This special session aims at putting together works in which optimization approaches and knowledge discovery are jointly concerned to solve problems coming from bioinformatics, biomedecine and neuroscience.

Topics of interest include, but are not limited to: 

• Original modeling and solving Bioinformatics, Biomedecine and Neuroscience optimization problems (for example: Folding, docking, protein interaction, network inference etc.) 
• Metaheuristics to solve knowledge discovery problems encountered in problems from these fields, such as classification, clustering, association rules, feature selection… 
• Knowledge discovery approaches embedded in metaheuristics to incorporate knowledge about the problem to be solved