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- Title
Cooperative Metaheuristics for Exploring Proteomic Data.
- Authors
Robin Gras; David Hernandez; Patricia Hernandez; Nadine Zangge; Yoann Mescam
- Abstract
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. The importance of information exchange in the optimization process is related to the building block hypothesis of evolutionary algorithms, which is based on these two questions: what is the pertinent information of a given potential solution and how this information can be shared? A classification of cooperative metaheuristics methods depending on the nature of cooperation involved is presented and the specific properties of each class, as well as a way to combine them, is discussed. Several improvements in the field of metaheuristics are also given. In particular, a method to regulate the use of classical genetic operators and to define new more pertinent ones is proposed, taking advantage of a building block structured representation of the explored space. A hierarchical approach resting on multiple levels of cooperative metaheuristics is finally presented, leading to the definition of a complete concerted cooperation strategy. Some applications of these concepts to difficult proteomics problems, including automatic protein identification, biological motif inference and multiple sequence alignment are presented. For each application, an innovative method based on the cooperation concept is given and compared with classical approaches. In the protein identification problem, a first level of cooperation using swarm intelligence is applied to the comparison of mass spectrometric data with biological sequence database, followed by a genetic programming method to discover an optimal scoring function. The multiple sequence alignment problem is decomposed in three steps involving several evolutionary processes to infer different kind of biological motifs and a concerted cooperation strategy to build the sequence alignment according to their motif content.
- Subjects
PROTEOMICS; COMBINATORIAL optimization; MOLECULAR biology; DISTRIBUTED artificial intelligence
- Publication
Artificial Intelligence Review, 2003, Vol 20, Issue 1/2, p95
- ISSN
0269-2821
- Publication type
Article
- DOI
10.1023/A:1026080413328