【摘要】 为克服准遗传算法收敛速度慢、早熟收敛等缺点,提出一种方向自学习遗传算法,该算法在局部搜索中引入方向信息,利用函数的伪梯度来指导搜索方向。算法通过个体之间的竞争、合作与学习来不断更新最优个体,为增加种群的多样性提出一种消亡算子,避免早熟收敛,提高算法收敛速度。采用4个二维函数和多个无约束高维函数对算法进行测试,与3个新提出的算法进行比较,实验数据和理论分析表明,该算法在解的质量上和计算复杂度上都优于上述3个算法,充分证明该算法的有效性。更多还原

【Abstract】 In order to overcome the disadvantages of classical genetic algorithm of low convergence speed and avoid prematurity,this paper proposes an improved Genetic Algorithm of Directional Self-Learning(DSLGA) . The directional information is introduced in local search process of the self-learning operator. And the search direction is guided by the false derivative of the function fitness. By the competition,cooperation and learning among the individuals,best solution is updated continuously. And a deletion operator is proposed to increase diversity. So the prematurity is avoided. In experiments,DSLGA is tested by four bi-dimension functions and three unconstrained benchmark problems,and the results are compared with CGA,MGA,FEP and OGA/Q. It shows that DSLGA performs much better than the above algorithms both in quality of solutions and in computational complexity. So the validity of the algorithm is obvious.更多还原