Analysis on Factors Affecting the Employment of Landless Peasants Based on Simulated Annealing Genetic Algorithm

Abstract

In national plan of twelfth five-year, orchestrating city-country development and accelerating process of urbanization will be one of the leading strategies. Large areas of farmland turn to non-agricultural irreversibly in the process of accelerating urbanization, the existing land expropriation system running by planned economic system damage farmers’ rights and incomes. Therefore, as vulnerable groups, landless peasants’ employments need more concern. The study takes Zhenjiang city for an example, proposes a factor selecting method based on the simulated annealing genetic algorithm (SAGA). The factor selecting method is proposed from each subspace, which combines the simulated annealing algorithm with the genetic algorithm in choosing different cross-over and mutation probabilities, as well as mutation individuals. Experimental results show that achieving a significant increase in the employment rate is possible using SAGA obtained from the proposed approach.