一种改进的自适应遗传算法

An Improved Adaptive Genetic Algorithm

为了提高遗传算法的搜索效率,给出了一种改进的遗传算法。该算法采用了混合编码,改进了适应度函数和交叉操作,扩大了搜索范围。通过四个经典函数的测试表明,改进算法与基本遗传算法和自适应遗传算法相比较,在函数最优值、平均收敛代数、收敛概率方面都取得了令人满意的效果。

In order to improve the searching efficiency of the genetic algorithm,an improved genetic algorithm （IAGA） is proposed. The algorithm adopts hybrid coding,does non-monotonic transformation to the fitness function and improves the crossover operation,expanding the searching scope. The test of four classic functions shows that the improved algorithm produces more satisfactory results on the best value,the average convergence generations and the convergence probability than the simple genetic algorithm （SGA） and the adaptive genetic algorithm （AGA）.