Abstract
A hybrid micro genetic algorithm (HMGA) is proposed for Pareto optimum search focusing on the Kursawe and ZDT test functions. HMGA is a fusion of the micro genetic algorithm (MGA) and the elitism concept of fast Pareto genetic algorithm. The effectiveness of HMGA in Pareto optimal convergence was investigated with two performance indicators (i.e. generational distance and spacing). To measure HMGA's performance, a comparison study was conducted between HMGA and MGA. In this work, HMGA is outperformed MGA in the search for Pareto optimal front and capable of solving different difficulty of MOPs.
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