Microarrays are rapidly becoming an important tool in livestock research. An efficient design is critical to ensure that the experiment will be able to address the relevant biological questions. Microarray experimental design is essentially a multicriteria optimization problem. For this class of problems Evolutionary Algorithms are well suited, as they can search the multicriteria solution space and evolve a design that optimizes the parameters of interest based on their relative value to the researcher under a given set of constraints. This paper introduces the use of Genetic Algorithms (GAs), a class of Evolutionary Algorithms, for optimization of experimental designs of spotted microarrays using a weighted multicriteria objective function. Evolved designs are compared with designs obtained through exhaustive search.

C. Gondro, Brian P Kinghorn

Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume , , 23.15, 2006
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