AbstractBiological databases are highly decentralized, having a high degree of difference in terminologies, feature fields, data representation and query formats. This is coupled by the problem of performing multi-database queries manually. Requirement arises therefore to automate the integration of biological databases that do much more than just retrieve and modify data. Speeding up the discovery of new medications and the introduction of new drugs in the market are some additional expectations out of such automation. Feature fields of different biological databases have different formats. To bind a meta-feature to the different feature formats under the same integration platform matching qualifiers is required for the different features. Integration requires binding formats with different databases concurrently, but the high dimensionality and redundancy of the qualifiers makes such integration impossible. Evolutionary selection algorithms have already been applied to reduce high dimensionality in microarray gene expression patterns. Given the similar qualifier redundancy and high qualifier dimensionality for biological databases such as EMBL, GENBANK and DDBJ, multi objective Genetic Algorithm applied to find qualifier reducts is not a misnomer. In feature binding initially Rough set theory is applied to find the initial population of qualifier reduct. Multi Objective Genetic Algorithm (NSGA-II) is run over this population to obtain the exact qualifier reduct. A feature set is categorized with the help of this qualifier reduct. Having done that, the problem of retrieving or manipulating data from a decentralized biological database is addressed in the Search & Retrieve algorithm, where stochastic and machine learning techniques have been used to find high probable warehouses where the data is indexed.