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In this paper, the performance assessment of the hybrid Archive-based Micro Genetic Algorithm (AMGA) on a set of bound-constrained synthetic test problems is reported. The hybrid AMGA proposed in this paper is a combination of a classical gradient based single-objective optimization algorithm and an evolutionary multi-objective optimization algorithm. The gradient based optimizer is used for a fast local search and is a variant of the sequential quadratic programming method. The Matlab implementation of the SQP (provided by the fmincon optimization function) is used in this paper. The evolutionary multi-objective optimization algorithm AMGA is used as the global optimizer. A scalarization scheme based on the weighted objectives is proposed which is designed to facilitate the simultaneous improvement of all the objectives. The scalarization scheme proposed in this paper also utilizes reference points as constraints to enable the algorithm to solve non-convex optimization problems. The gradient based optimizer is used as the mutation operator of the evolutionary algorithm and a suitable scheme to switch between the genetic mutation and the gradient based mutation is proposed. The hybrid AMGA is designed to balance local versus global search strategies so as to obtain a set of diverse non-dominated solutions as quickly as possible. The simulation results of the hybrid AMGA are reported on the bound-constrained test problems described in the CEC09 benchmark suite.