initial population造句
例句與造句
- Change tracking population requires an initial population of the full - text in question
更改跟蹤填充要求對相應(yīng)的全文進(jìn)行了初步填充。 - If you make the rules such that it s too hard to gain fitness , your initial population doesn t stand a chance
如果您制定的規(guī)則使得適應(yīng)性很難獲取，則您的初始種群就沒有增長的希望。 - Horizontal partition for initial population construction can guarantee the feasibility and quality of initial solution
種群初始化技術(shù)采用均衡水平分割法，以保證初始個體的有效性和質(zhì)量。 - From the randomized initial population , using the operators such as select , crossover and mutation , it can converge the globe optimum solution
從隨機(jī)產(chǎn)生的初始種群出發(fā)，通過選擇、雜交及變異等遺傳算子，使種群一代一代地進(jìn)化到最優(yōu)解。 - Evolutionary algorithm starts from initial population . the individuals of each population embed some new information while the searching can be directed to the promising area
演化算法從種群開始搜索，每個種群所獲得的知識都被嵌入了其成員的染色體中。 - It's difficult to find initial population in a sentence. 用initial population造句挺難的
- The indexing component of full - text search is responsible for the initial population of the full - text index , and the subsequent update of this index when the data in the full - text indexed tables is modified
全文搜索的索引組件負(fù)責(zé)對全文索引進(jìn)行初始填充，并負(fù)責(zé)在全文索引表中的數(shù)據(jù)有所修改時對該索引進(jìn)行后續(xù)更新。 - At last , a new algorithm is proposed to generate automaticlly the optimal initial population for disassembling sequence , which improves the efficiency for genetic algorithms to search disassembling sequences of an assembly
然后，提出一種新的計算模型，據(jù)此計算各個零件從裝配體上可以拆卸下來的方向，從而確定拆卸序列在幾何上的可行性。 - Wen stopped introducing the concept of a barrier - free path to simplify the initial population have , also facilitate the crossover and mutation genetic operator , the operation so that the genetic algorithm in robot path planning application more convenient
文中引入間斷無障礙路徑的概念以簡化初始種群產(chǎn)生,也方便了交叉和變異等遺傳算子的操作，從而使遺傳算法在機(jī)器人路徑規(guī)劃中應(yīng)用更方便。 - Then according to the request of algorithm and characteristic of fpga , the module was divided and the circuit structure was designed , this module includes these functions as follows : initialing population 、 calculating fitness 、 reproduction 、 corssover 、 mutation and population storing
然后依據(jù)算法要求以及現(xiàn)場可編輯門陣列( fpga )的特點進(jìn)行模塊劃分與電路結(jié)構(gòu)設(shè)計,具體包括初始化群體、適應(yīng)度計算、選擇、交叉、變異、群體存儲等功能模塊。 - Research and application of genetic expression programming algorithm based on uniform - design a new evolutionary algorithm named uniformdesign genetic expression programming ugep based on uniformdesign was proposed , which improved the way of generating initial population and also improved hybrid crossover operators . theoretic analysis proves . .
提出了一種基于均勻設(shè)計的基因表達(dá)式編程算法ugep ，該算法對經(jīng)典的gep算法做了以下改進(jìn)：利用混合水平均勻表的構(gòu)造對初始種群的產(chǎn)生進(jìn)行改進(jìn)，保證了解分布的均勻性引入自適應(yīng)多親雜交算子，用均勻優(yōu)化代替隨機(jī)進(jìn)化。 - Aimed at the shortage of ga , this paper improves the simple genetic algorithm and accomplishes the optimal program based on the adaptive genetic algorithm . the result shows that the adaptive genetic algorithm improves the convergence rate . generally , the genetic algorithm randomly generates the initial population
針對簡單遺傳算法的缺點，本論文提出了對簡單遺傳算法進(jìn)行改進(jìn)?自適應(yīng)遺傳算法，并且編制了基于自適應(yīng)遺傳算法的結(jié)構(gòu)優(yōu)化程序，算例分析表明得出自適應(yīng)遺傳算法確實提高了遺傳算法在應(yīng)用于結(jié)構(gòu)優(yōu)化設(shè)計方面收斂性和計算速度。 - A modified genetic algorithm ( mga ) framework was developed and applied to the flowshop sequencing problems with objective of minimizing mean total flowtime . to improve the general genetic algorithm routine , two operations were introduced into the framework . firstly , the worst points were filtered off in each generation and replaced with the best individuals found in previous generations ; secondly , the most promising individual was selectively cultivating if a certain number of recent generations have not been improved yet . under conditions of flowshop machine , the initial population generation and crossover function can also be improved when the mga framework is implemented . computational experiments with random samples show that the mga is superior to general genetic algorithm in performance and comparable to special - purpose heuristic algorithms . the mga framework can also be easily extended to other optimizations even though it will be implemented differently in detail
提出了一個改進(jìn)遺傳算法的結(jié)構(gòu)，并且應(yīng)用于帶有目標(biāo)是最小平均總流程時間的流水調(diào)度排序中.為了改進(jìn)一般遺傳算法的程序，兩個新的操作被引進(jìn)到這個操作中.這兩個操作為： 1 )過濾操作：過濾掉在每一代中的最壞的個體，用前一代中的最好的個體替代它； 2 )培育操作：當(dāng)在一定代數(shù)內(nèi)算法不改進(jìn)時，選擇一個培育操作用于培育最有希望的個體.通過大量的隨機(jī)產(chǎn)生的問題的例子的計算機(jī)實驗顯示出，提出的算法的性能明顯好于一般遺傳算法，并且和此問題的最好的專門意義的啟發(fā)式算法相匹配.新的mga框架很容易擴(kuò)展到其它最優(yōu)化當(dāng)中，只是實施的詳細(xì)的步驟有所不同 - In the second charper , two reformed metheds has presented , which are competition adaptation - ga based on elitist and dual mutation adaptive - ga which can increase the population diversity and can decrease the dependence of genetic algorithms " result to the control parameter of operators and the status of initial population . to testify their abilities of algorithms , some studies have excuted which included the study to optimize parameter for pid and the study to multivariable intelligent decoupling control for mimo system . the outcomes have showed us that this amendment has a better effective than conventional means and the genetic algorithms which have no change
第二章中，在已有文獻(xiàn)的基礎(chǔ)上，創(chuàng)新性的提出兩種改進(jìn)算法：基于最優(yōu)保留的聯(lián)賽競爭機(jī)制遺傳算法( competitionadaptation - gabasedonelitist )和雙變異自適應(yīng)遺傳算法( dualmutationadaptive - ga ) ；通過對算法中選擇算子和變異算子的有效改進(jìn)，提高了算法的尋優(yōu)能力和尋優(yōu)效率，增加了群體中個體模式的多樣性，對于算法中存在的欺騙問題、早熟問題以及成熟前收斂問題有明顯的改進(jìn)作用。
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