英语翻译3.PROPOSED ALGORITHM The proposed dynamic evolutionary algorithm exploits evolutionary progress history to adapt the current population to the new environment.The algorithm introduces shifts in the decision space,the so-called relocations

英语翻译3.PROPOSED ALGORITHM The proposed dynamic evolutionary algorithm exploits evolutionary progress history to adapt the current population to the new environment.The algorithm introduces shifts in the decision space,the so-called relocations
英语翻译
3.PROPOSED ALGORITHM
The proposed dynamic evolutionary algorithm exploits evolutionary progress history to adapt the current population to the new environment.The algorithm introduces shifts in the decision space,the so-called relocations,which try to account for the estimated amount of change in the objective space.A complete description of the proposed algorithm is presented in detail next.represents the DOP we want to optimize; represents the -dimensional decision vector,and denotes the th-dimension decision variable.A nomenclature table is given in Appendix A for quick reference.For the discussion of this paper,the DOP is assumed to be a maximization problem.Note that a minimization problem can be converted into a maximization problem by multiplying with .denotes the child’s evolutionary progress in the th-dimension of the decision variable with respect to its parents.It is measured as the difference between the th-dimension decision variable of a child and that of the centroid of its parents.This can be mathematically expressed as (2) The evolutionary fitness progress,,of a child with respect to its parents is measured as the difference between the fitness,,of a child and the interpolated fitness of its parents.The interpolation is based on the distance between a child and its parents .The farther a parent is from its child,the lesser is its contribution to the interpolated fitness,as shown in (3a)–(4) at the bottom of the page.The average evolutionary progress in the th-dimension decision variable of a child can be obtained as the weighted sum of the child’s and the average evolutionary progress in the th-dimension decision variable of its parents.The same is true for the child’s average evolutionary fitness progress ,except that we use the interpolated value of its parents’ average fitness progress,as shown in (5) and (6) at the bottom of the next page,where denotes the total number of generations either from the start of an evolutionary process or the last occurrence of change,whichever was more recent,up to the current generation.On the other hand,represents the weight given to previous evolutionary progresses relative to the current one.Using ,the weighted average is equivalent to taking a simple average of all the individual progresses (total sum divided by number of generation).This means that the effect of all the previous and values starting from the recent occurrence of change is accumulated and does not diminish through time.

英语翻译3.PROPOSED ALGORITHM The proposed dynamic evolutionary algorithm exploits evolutionary progress history to adapt the current population to the new environment.The algorithm introduces shifts in the decision space,the so-called relocations
3.算法
提出利用动态进化算法演化过程的历史上适应当前人口向新的环境.该算法制决定了空间,所谓的重定位,试著去说明预算金额的改变的目的空间.的完整描述了该算法的详细介绍了next. DOP我们想优化决策;代表维向量,表示了th-dimension决策变量.一个命名法在附录A表给出了快速参考.讨论了增塑剂DOP被认为是一个最大化问题.需要指出的是,最小化问题能被转换成最大化问题.是指乘以孩子的演化过程的决定th-dimension变量相对于他的父母.这是成为衡量th-dimension决策变量之间的差距的孩子和质心的父母.这可以被数学表示为(2)进化健身的进步,、从小的时候就其父母量之间的差异,健身,孩子和插入的状态良好的他的父母.插值是基于距离的孩子和他的父母.父母越远从它的孩子,这是驳不倒的健身插值做出了贡献,显示在(3a)-(4)这页纸的底部.在演化过程平均的决策变量th-dimension获得能使一个小孩子加权和的孩子的,平均演化过程在th-dimension决策变量的父母.这同样适用于孩子的平均进化健身的进步,除了我们使用插值值父母的平均健身进步,显示在(五)、(六),在底部的页面,在总数量是指可以从几代人的evolu开始的时候