probability of crossover造句
例句與造句
- Auto - - generating paper is a constrained multi - object optimization problem . this paper presents a way based on genetic algorithm ( ga ) to solve the problem . we define the crossover operator and mutation operator in the real coded and adjust the probabilities of crossover and mutation
系統(tǒng)實(shí)現(xiàn)采用三層組織結(jié)構(gòu),面向?qū)W習(xí)者、教師、管理員三類用戶,具有課程學(xué)習(xí)、作業(yè)、答疑、考試以及交流功能,同時(shí)采用java語(yǔ)言來(lái)構(gòu)建智能agent ,解決了個(gè)性化學(xué)習(xí)的問(wèn)題。 - On the vsp with time window , while the restraints of capacity and time windows are changed into object restraints , a mathematic model is established . we use technique such as maximum preserved crossover and selfadaptability change of probability of crossover and mutation , and design genetic algorithm on nature number , which can deal with soft and hard time windows . the excellent solutions are obtained in the application
對(duì)于有時(shí)間窗的非滿載vsp問(wèn)題，將貨運(yùn)量約束和時(shí)間窗約束轉(zhuǎn)化為目標(biāo)約束，建立了vsp模型，使用最大保留交叉、交叉率和變異率的自適應(yīng)調(diào)整等技術(shù)，設(shè)計(jì)了給予自然數(shù)編碼的可同時(shí)處理軟、硬時(shí)間窗約束的遺傳算法，實(shí)驗(yàn)分析取得了較好的結(jié)果。 - Perfection and adjustment according to system properties , it combines genetic algorithms with fuzzy control , detailed analyzes the problem of designing fuzzy controller and proposes two advanced schemes : first scheme : the change - of - variables are emerged into input variables of the simple fuzzy controllers of oil feeding pump system as one variable , and one pi block is connected after output of fuzzy controllers , consequently the structure of the improved fuzzy controller is analyzed , finally genetic algorithms with adaptive probabilities of crossover and mutation is applied to optimize membership functions and fusing factors of the fuzzy controllers , and the simulation results of before and after optimization are compared
由于在模糊控制器的設(shè)計(jì)過(guò)程中存在較多的人為因素，為了實(shí)現(xiàn)根據(jù)系統(tǒng)特性對(duì)模糊規(guī)則和隸屬函數(shù)進(jìn)行自動(dòng)修正、完善和調(diào)整，本文將遺傳算法和模糊控制結(jié)合起來(lái)，并針對(duì)前面設(shè)計(jì)的模糊控制器中所存在的問(wèn)題進(jìn)行了詳細(xì)分析，提出了兩種改進(jìn)方案： 1在簡(jiǎn)單模糊控制器的輸入變量中加入了變量變化率的信息，即將輸入變量和變量的變化率融合為一個(gè)輸入量，并在模糊控制器的輸出端加入比例、積分環(huán)節(jié)，然后分析了這種改進(jìn)后的模糊控制器的解析結(jié)構(gòu)，最后采用改進(jìn)后的自適應(yīng)遺傳算子的遺傳算法對(duì)模糊控制器中的隸屬函數(shù)和融合因子進(jìn)行優(yōu)化，并將優(yōu)化前后的結(jié)果作了比較和分析。 2 - The study work mainly included the following : ( 1 ) to overcome the premature convergence of the standard genetic algorithm ( sga ) , a genetic algorithm ( ga ) based on fuzzy reasoning was proposed . this method was used to adjust the probabilities of crossover and mutation during the evolution of ga
本文的研究工作主要包括: ( 1 )針對(duì)標(biāo)準(zhǔn)遺傳算法( sga )的未成熟收斂現(xiàn)象,采用模糊推理運(yùn)算的方法確定遺傳算法中的交叉概率和變異概率,實(shí)現(xiàn)交叉概率和變異概率的動(dòng)態(tài)調(diào)整,從而改善遺傳算法的性能。 - Methods to manage each constraint condition were put forward , aiming at the premature and slow convergence of genetic algorithm , this algorithm introduced the combination of genetic algorithm and simulated annealing technology , combining with self - adaptive probabilities of crossover and mutation
提出了對(duì)各約束條件處理的方法，針對(duì)遺傳算法收斂慢等的不足，提出將遺傳算法和模擬退后技術(shù)相結(jié)合，并采用自適應(yīng)交叉和變異率的解決方法。同時(shí)也考慮了混合泵站采用遺傳算法求解的具體實(shí)現(xiàn)步驟和算法。 - It's difficult to find probability of crossover in a sentence. 用probability of crossover造句挺難的
- The improvements in this thesis include the hybrid code method , the method of generation of initial populations , substituting the children for parents by combination of the simulated annealing algorithm and niche , adding some new chromosomes to ensure the population ’ s diversity and using the adaptive probability of crossover and mutation
本文的改進(jìn)方案主要涉及混合編碼方式,初始種群的產(chǎn)生方式,采用結(jié)合模擬退火算法和小生境思想的替代策略,迭代過(guò)程中添加新染色體,采用自適應(yīng)交叉、變異概率等。