%進化策略算法?（μλ）-ES
%μ個父代進化產生λ個子代，新產生的λ個新個體中擇優選擇μ個個體作為下一代群體
%f（x）=x1+x2+x3+x4+x5+x6
clear?all;
close?all;
clc
%變量初始化
populationSize?=?15;???????%種群大小及子代個數?比例1/7
childrenSize?=?100;
best=0;????????????????????%存放父代最優值及所在列
bestIndex=1;
fitness?=?zeros（?childrenSize?13）;????%存放子代?其中（1:6）存放子代個體6個分量，（7:12）存放子代個體6個方差，（113）存放子代的適應度函數
population=zeros（populationSize?13）;??%存放父代
%?初始化父代
for?i?=?1?:?populationSize
????population（i?1）?=?round（rand?*?60）;
????population（i?2）?=?round（rand?*?70）;
????population（i?3）?=?round（rand?*?60）;
????population（i?4）?=?round（rand?*?50）;
????population（i?5）?=?round（rand?*?20）;
????population（i?6）?=?round（rand?*?30）;
????
????while?（population（i?1）+population（i?6））<60||（population（i?1）+population（i?2））<70||...
??????????（population（i?3）+population（i?2））<60||（population（i?4）+population（i?3））<50||...
???????（population（i?5）+population（i?4））<20?||?（population（i?6）+population（i?5））<30
????????population（i?1）?=?round（rand?*?60）;
????????population（i?2）?=?round（rand?*?70）;
????????population（i?3）?=?round（rand?*?60）;
????????population（i?4）?=?round（rand?*?50）;
????????population（i?5）?=?round（rand?*?20）;
????????population（i?6）?=?round（rand?*?30）;
????end
????for?j=7:13
????????population（ij）=3.0;%sigma初始化
????????if?j==13
????????????population（i?13）?=?population（i?1）+population（i?2）+population（i?3）+population（i?4）+population（i?5）+?population（i?6）;
????????end
????end
????if?best>population（i?13）
???????best=population（i13）;
???????bestIndex=i;
????end
end
bestFitness=population（bestIndex:）;%精英保留
%?進行100次迭代
for?t?=?1?:?100
????%?產生childrenSize個子代，并計算其方差、適應度保存到fitness數組中
????for?i=1:childrenSize
????????populationIndex?=?round（rand?*?（populationSize-1））+1;%?從父代中隨機挑選一個用于產生子代?
????????
????????for?j=1:6
????????????fitness（i6+j）=population（populationIndex6+j）?*?exp（1?*?normrnd（0?1）?+?1?*?normrnd（0?1））;?%?調整sigma參數
????????????fitness（ij）=round（population（populationIndex?j）?+?fitness（i6+j）?*normrnd（0?1））;
????????end
????????fitness（i13）=fitness（i1）+fitness（i2）+fitness（i3）+fitness（i4）+fitness（i5）+fitness（i6）;
????????%根據條件產生符合條件的子代
????????while?fitness（i1）??60?||?fitness（i6）??60?||?（fitness（i1）+fitness（i6））<60?||...
????????????（fitness（i1）+fitness（i2））<70?||?fitness（i2）<0?||?fitness（i2）>70?||?（fitness（i3）+fitness（i2））<60?||?fitness（i3）<0?||?fitness（i3）>60||...
????????????（fitness（i3）+fitness（i4））<50?||?fitness（i4）<0?||?fitness（i4）>50?||?（fitness（i5）+fitness（i4））<20?||...?
????????????（fitness（i6）+fitness（i5））<30?||?fitness（i5）<0?||?fitness（i5）>30
????????????for?j=1:6
????????????fitness（i6+j）=population（populationIndex6+j）?*?exp（1?*?normrnd（0?1）?+?1?*?normrnd（0?1））;?%?調整sigma參數
????????????fitness（ij）=round（population（populationIndex?j）?+?fitness（i6+j）?*normrnd（0?1））;
????????????end??
????????????fitness（i13）=fitness（i1）+fitness（i2）+fitness（i3

?屬性????????????大小?????日期????時間???名稱
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?????文件??????91648??2014-03-21?19:37??EvolutionStrategy\BusDispatch.doc

?????文件???????3624??2013-06-19?21:54??EvolutionStrategy\EvolutionStrategy.m

?????目錄??????????0??2014-03-21?19:51??EvolutionStrategy

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