The first section is the introduction on gas " theory and its applications on limit value of function optimization 第一部分介紹遺傳算法的理論和它在函數(shù)極值優(yōu)化問(wèn)題中的應(yīng)用。
A new type of improved gas - hybrid genetic algorithm ( hga ) is put forward , and its program is designed to solve the problem of function optimization 提出了一種新的改進(jìn)遺傳算法?混合式遺傳算法,設(shè)計(jì)了解決函數(shù)優(yōu)化混合式遺傳算法運(yùn)行程序。
Generally the problem of learning the parameters of fuzzy neural networks may change to the problem of function optimization 一般來(lái)說(shuō),對(duì)模糊神經(jīng)網(wǎng)絡(luò)的參數(shù)學(xué)習(xí)問(wèn)題可以轉(zhuǎn)化為對(duì)其目標(biāo)函數(shù)的優(yōu)化問(wèn)題,即尋找一組合適的參數(shù)向量使其目標(biāo)函數(shù)值最優(yōu)。
The simulation result of complicated function optimization shows that this improved crossover operation is much more effective than the standard crossover operation 對(duì)復(fù)雜函數(shù)優(yōu)化的仿真計(jì)算結(jié)果表明,同標(biāo)準(zhǔn)交叉操作比較,改進(jìn)的交叉操作更加有效。
The applied example of function optimization and calculation result indicate that sa - pso method can improve the seeking the global excellence and its stability 通過(guò)求解函數(shù)優(yōu)化問(wèn)題對(duì)比實(shí)驗(yàn),表明改進(jìn)后的粒子群優(yōu)化算法增強(qiáng)全局尋優(yōu)能力,搜索成功率大為提高。
Two representative examples of function optimization are given to show the higher efficiency . further , we investigate a first - order neural network model with a discrete delay 為了檢驗(yàn)網(wǎng)絡(luò)的優(yōu)化效果,我們將這兩種網(wǎng)絡(luò)應(yīng)用到了典型的函數(shù)優(yōu)化中,結(jié)果比較令人滿(mǎn)意。
A mixed ep - es evolutionary algorithm for real - valued function optimization is proposed . numerical results illustrate that the proposed algorithm is efficient . 3 針對(duì)實(shí)值連續(xù)函數(shù)優(yōu)化問(wèn)題,提出了一種混合的ep - es進(jìn)化算法,典型數(shù)值實(shí)驗(yàn)表明,所提出的算法是可行的、有效的。
Then , we investigated the epistasis of gas using walsh - schema transform , and further evaluated the epistasis order for the < wp = 5 > continuous function optimization problems 利用walsh模式變換對(duì)遺傳算法基因關(guān)聯(lián)問(wèn)題進(jìn)行了分析,并對(duì)連續(xù)函數(shù)優(yōu)化問(wèn)題的基因關(guān)聯(lián)階數(shù)進(jìn)行了估計(jì)。
It has been used to tackle complex questions such as combination optimization , function optimization and machine learning , and has aroused many researchers " concerns and interest 進(jìn)化算法已經(jīng)廣泛地應(yīng)用到組合優(yōu)化、函數(shù)優(yōu)化、機(jī)器學(xué)習(xí)等復(fù)雜的問(wèn)題中,并引起許多學(xué)者的關(guān)注和興趣。