Cooperative Metaheuristics for Exploring Proteomic Data

Most combinatorial optimization problems cannot be solved exactly. A class of methods, called metaheuristics, has proved its efficiency to give good approximated solutions in a reasonable time. Cooperative metaheuristics are a sub-set of metaheuristics, which implies a parallel exploration of the search space by several entities with information exchange between them. Several improvements in the field of metaheuristics are given. A hierarchical approach resting on multiple levels of cooperative metaheuristics is presented. Some applications of these concepts to difficult proteomics problems, including automatic protein identi- fication, biological motif discovery and multiple sequence alignment are presented. For each application, an innovative method based on the cooperation concept is given and compared with classical approaches.