Abstract
This paper describes a new multiobjective GA named Adaptive Range Multiobjective GA (ARMOGA). Adaptive Range GA (ARGA) has been extended to multiobjective optimization problems. It helps GAs to handle a large search space that requires continuous sampling. ARGA has two characteristics : de-coding based on Gaussian random number and adaptive search space renewed at a certain interval. The resulting AEMOGA is compared with the conventional MOGA by using test problems. The results confirm that ARMOGA consistently finds better solutions than the conventional MOGA.
