Cooperative Metaheuristics for Exploring Proteomic Data
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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.
KeywordsSearch Space Evolutionary Algorithm Multiple Sequence Alignment Relative Entropy Spectrum Graph
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