Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Parallel programming with MPI
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
The ant colony optimization meta-heuristic
New ideas in optimization
On approximation algorithms for local multiple alignment
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
How to solve it: modern heuristics
How to solve it: modern heuristics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Finding motifs in the twilight zone
Proceedings of the sixth annual international conference on Computational biology
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Combinatorial Approaches to Finding Subtle Signals in DNA Sequences
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Genetic Programming: A Parallel Approach
Soft-Ware 2002 Proceedings of the First International Conference on Computing in an Imperfect World
Experimental Study of Multipopulation Parallel Genetic Programming
Proceedings of the European Conference on Genetic Programming
MoDEL: an efficient strategy for ungapped local multiple alignment
Computational Biology and Chemistry
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Most combinatorial optimization problems cannotbe solved exactly. A class of methods, calledmetaheuristics, has proved its efficiency togive good approximated solutions in areasonable time. Cooperative metaheuristics area sub-set of metaheuristics, which implies aparallel exploration of the search space byseveral entities with information exchangebetween them. The importance of informationexchange in the optimization process is relatedto the building block hypothesis ofevolutionary algorithms, which is based onthese two questions: what is the pertinentinformation of a given potential solution andhow this information can be shared? Aclassification of cooperative metaheuristicsmethods depending on the nature of cooperationinvolved is presented and the specificproperties of each class, as well as a way tocombine them, is discussed. Severalimprovements in the field of metaheuristics arealso given. In particular, a method to regulatethe use of classical genetic operators and todefine new more pertinent ones is proposed,taking advantage of a building block structuredrepresentation of the explored space. Ahierarchical approach resting on multiplelevels of cooperative metaheuristics is finallypresented, leading to the definition of acomplete concerted cooperation strategy. Someapplications of these concepts to difficultproteomics problems, including automaticprotein identification, biological motifinference and multiple sequence alignment arepresented. For each application, an innovativemethod based on the cooperation concept isgiven and compared with classical approaches.In the protein identification problem, a firstlevel of cooperation using swarm intelligenceis applied to the comparison of massspectrometric data with biological sequencedatabase, followed by a genetic programmingmethod to discover an optimal scoring function.The multiple sequence alignment problem isdecomposed in three steps involving severalevolutionary processes to infer different kindof biological motifs and a concertedcooperation strategy to build the sequencealignment according to their motif content.