Difference between revisions of "Initialisation"
From Adaptive Population based Simplex
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N=max | N=\max\left(40+2\sqrt{D},\sqrt{40^{2}+\left(D+2\right)^{2}}\right)\label{eq:N_popsize} | ||
| − | where D is the dimension of the search space. Note that N needs to be at least equal to $D+ | + | where D is the dimension of the search space. Note that N needs to be at least equal to $D+ |
| − | + | ||
| − | + | ||
We will need the volume V(0) of the previous simplex. As no one has been defined yet, we simply set it to 0:<br /> | We will need the volume V(0) of the previous simplex. As no one has been defined yet, we simply set it to 0:<br /> | ||
V(0)=0 | V(0)=0 | ||
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| + | === Population cost === | ||
| + | Evaluate the N individuals. Save the best one as Best. | ||
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| + | The sum of all values (they are all supposed to be positive, which is always possible), is the initial ''population cost'' C. We are trying here to minimise it. | ||
Revision as of 18:44, 28 June 2013
Basic initialisation (i0)
Draw at random N agents (positions) in the search space, according to an uniform distribution.
N=\max\left(40+2\sqrt{D},\sqrt{40^{2}+\left(D+2\right)^{2}}\right)\tag{1}
where D is the dimension of the search space. Note that N needs to be at least equal to D+ We will need the volume V(0) of the previous simplex. As no one has been defined yet, we simply set it to 0: UNIQd3300b0b71cfbd96-MathJax-1-QINU === Population cost === Evaluate the N individuals. Save the best one as Best. The sum of all values (they are all supposed to be positive, which is always possible), is the initial ''population cost'' C$. We are trying here to minimise it.
