Improvement of Selection and Crossover Strategy in Genetic Algorithm
Yan Ma · Jisuanji gongcheng · 2008
This paper proposes an improved Genetic Algorithm(GA). The ranking selection intensity adopts an adaptive adjusting mechanism, which can adjust the selection intensity dynamically according to the change of the population state. A new crossover strategy which chooses the outstanding individuals according to competition is used to increase the individual average performance of the population. The simulation with the typical test functions indicates that this algorithm can improve the precision of solutions and convergence speed of simple genetic algorithm, and the proportion of convergence can reach more than 90%.