Difference between revisions of "Local search"
From Adaptive Population based Simplex
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=== Random choice === | === Random choice === | ||
The distribution that is used is itself selected at random (uniform distribution) between two ones: | The distribution that is used is itself selected at random (uniform distribution) between two ones: | ||
| − | # the uniform one. The components of the direction vector follow the normalised Gaussian distribution, and the radius is $r=\rho*rand(0,1)^{\frac{1/D}}$ | + | # the uniform one. The components of the direction vector follow the normalised Gaussian distribution, and the radius is $r=\rho*rand(0,1)^{\frac{1/D}{}}$ |
# a non uniform one (more dense near to the centre). Here the radius is simply $r=\rho*rand(0,1)$. | # a non uniform one (more dense near to the centre). Here the radius is simply $r=\rho*rand(0,1)$. | ||
Revision as of 18:13, 28 June 2013
Basic local search (l0)
The idea is just to perform a random search "around" the current best point. So the process is the following:
- Compute the maximum distance $\rho$ between $x_{best}$ and all the other individuals of the population.
- Select the $x_l$ position at random in the hypersphere of centre $x_{best}$ and of radius $\rho$. The basic random choice makes use of two distributions, an uniform one, and a non-uniform one (see below).
If $x_{l}$ is better than $x_i$, decrease the population cost
$$ C\leftarrow C-f(x_i)+f(x_l)$$
If $x_{l}$ is better than the best ever found (i.e. $Best$), set $Best=x_{l}$.
Random choice
The distribution that is used is itself selected at random (uniform distribution) between two ones:
- the uniform one. The components of the direction vector follow the normalised Gaussian distribution, and the radius is $r=\rho*rand(0,1)^{\frac{1/D}{}}$
- a non uniform one (more dense near to the centre). Here the radius is simply $r=\rho*rand(0,1)$.
