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A multi-objective genetic algorithm for the design of biorthogonal filter banks for embedded image coding application is presented. To be effective, the filter bank would satisfy multiple requirements related to such application. Flexibility in the design is introduced by imposing Near Perfect Reconstruction (N-PR) condition instead of entire PR condition as in conventional designs. Especially for embedded coding purposes, the filter banks are designed to be near-orthogonal. This can only be made possible by minimizing the deviation from the orthogonality in the optimization process. The optimization problem is formulated as a constrained multi-objective problem and solved using a constrained Non-dominated sorting genetic algorithm (C-NSGA) by searching solutions that achieve the best compromise between the different objective criteria, these solutions are known as Pareto Optimal Solutions. Experiment results show that our designed filter banks lead to improved performances of image coding compared to those achieved by the 9/7 filter bank of JPEG2000.