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Evolution and Optimum Seeking

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A Multimembered <strong>Evolution</strong> Strategy 127<br />

1.0<br />

0.9<br />

0.8<br />

0.7<br />

0.6<br />

0.5<br />

0.4<br />

0.3<br />

0.2<br />

0.1<br />

0<br />

Probability density<br />

w(s’) for = 1<br />

Parameter = number<br />

of offspring<br />

one obtains from the requirement<br />

the relation<br />

= 1<br />

3<br />

−4 −2 0 2 4<br />

Useful distance s’<br />

10<br />

30<br />

100<br />

Figure 5.7: Probability distribution w(s 0 )<br />

@<br />

@<br />

~'<br />

= opt<br />

opt =~' @<br />

@ ~' = opt<br />

!<br />

=0<br />

<strong>and</strong>, by substituting it back in Equation (5.17), the result<br />

opt =1+<br />

s<br />

The value obtained iteratively is<br />

5.2.2.2 The Sphere Model<br />

2 opt<br />

exp<br />

1<br />

2 opt<br />

! 2<br />

=<br />

2<br />

~' 2<br />

0<br />

41+ erf@<br />

1<br />

q<br />

2 opt<br />

opt ' 2:5 (as an integer: opt =2or3)<br />

We willnowtry to calculate the rate of progress for the simple spherically symmetrical<br />

model, which is of importance for considering the convergence rate properties of the<br />

strategy. The contours of the objective function F (x) are concentric hypersphere surfaces,<br />

given for example by<br />

F (x) =<br />

nX<br />

i=1<br />

x 2<br />

i = const:<br />

13<br />

A5

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