A Quadratic Approximation-Based Local Search Procedure for Multiobjective Genetic Algorithms

We devise in this paper a local search procedure for multiobjective genetic algorithms (GAs). The proposed local search process employs quadratic approximations for all objective functions involved in the optimization problem. The samples gathered by the algorithm along the evolutionary process are used to fit these quadratic approximations around the point selected to local search, therefore no extra cost of function evaluation is required. After that, a locally improved solution is easily estimated from the quadratic associated problem. We demonstrate the hybridization of our proposed procedure with SPEA 2.