Abstract
The genetic algorithm (GA) is a method for approximate optimization simulating the process of natural evolution, and it has been successfully applied to several optimization problems which are difficult to solve exactly by conventional methods of the mathematical programming. This paper proposes a neighborhood model of GA (NGA) where the selection is executed locally in a neighborhood of each population. The objectives of the NGA are to improve search in the GA by suppressing favorably the premature convergence phenomena, and at the same time, to reduce computational time by implementing it on a parallel computer (transputer). In the paper, we confirm effectiveness of the NGA through several computational experiments to a jobshop scheduling problem where its ability in computational time and quality of solutions is investigated.
