自适应遗传算法

在遗传算法中约束条件贯穿于遗传运算的始终,这样必定影响运算效率。因为随着进化过程的进行,适应度较低的一些个体逐渐被淘汰,而适应度较高的个体越来越多,且都集中在最优点附近。基于遗传算法这种优胜劣汰的进化思想,该文提出一种改进的遗传算法——自适应遗传算法。其主要思想是在群体进化若干代后,将弱解空间删除,在以后的进化进程中以同样的群体大小只在强解空间进行群体的繁殖,则可加大强解空间的个体密度,提高解的精度,这样有助于性能优良的个体的产生,并且有可能缩短群体进化过程。将这种自适应遗传算法用于复杂函数的优化,算例结果表明该方法是有效和可靠的。

Two New Characterizations of H^p(D) Functions

In this paper,two new characterizations of H^p(D) functions are given.

Lower Semicontinuity of the Effcient Solution Mapping in Semi-In?nite Vector Optimization

This paper establishes some suffcient conditions for the lower semicontinuity of the effcient solution mapping for the semi-infinite vector optimization problem with perturbations of both the objective function and the constraint set in normed linear spaces. The constraint set is the set of weakly effcient solutions of vector equilibrium problem, and perturbed by the perturbation of the criterion mapping to the vector equilibrium problem.

Weak Mixed Martingale Hardy Spaces

In this paper,the authors introduce weak mixed martingale Hardy spaces defined with the help of weak mixed L^(p→) norm.The authors establish atomic decompositions of weak mixed martingale Hardy spaces and give the sufficient conditions for the boundedness ofσ-sublinear operators on weak mixed martingale Hardy spaces.As an application,the inclusion relations of different weak mixed martingale Hardy spaces are easily proved.