Local search - Revision history

Admin: Admin moved page Last Chance / Local search to Local search without leaving a redirect: revert

2015-09-22T08:06:37Z

Admin moved page Last Chance / Local search to Local search without leaving a redirect: revert

Admin: Admin moved page Local search to Last Chance / Local search without leaving a redirect

2015-09-22T08:01:48Z

Admin moved page Local search to Last Chance / Local search without leaving a redirect

Editor: /* Basic local search (l0) */

2013-06-28T17:53:59Z

‎Basic local search (l0)

Editor: /* Basic local search (l0) */

2013-06-28T17:53:01Z

‎Basic local search (l0)

Editor: /* Basic local search (l0) */

2013-06-28T17:48:16Z

‎Basic local search (l0)

Editor: /* Random choice */

2013-06-28T17:14:18Z

‎Random choice

Editor: /* Random choice */

2013-06-28T17:13:26Z

‎Random choice

Editor at 17:12, 28 June 2013

2013-06-28T17:12:32Z

MClerc: Created page with "TO COMPLETE == Basic local search (l0) == Define a hypersphere around the best point of the three ones defined in the selection phase.
Radius= maximum distance to the o..."

2013-06-12T20:05:59Z

Created page with "TO COMPLETE == Basic local search (l0) == Define a hypersphere around the best point of the three ones defined in the selection phase.<br /> Radius= maximum distance to the o..."

New page

TO COMPLETE

== Basic local search (l0) ==
Define a hypersphere around the best point of the three ones defined in the selection phase.<br />
Radius= maximum distance to the other vertices of the simplex

The test position is chosen at random in the hypersphere. Random direction (uniform), random radius (uniform), which implies that the distribution is '''not''' random (more dense near to the centre)

@@ Line 1: / Line 1: @@
 == Basic local search (l0) ==
-The idea is to perform a random search "around" a good point, but only if the current point is worse than average, i.e. if $C>\frac{f(x_i)}{N}$. In that case, the process is the following:
+The idea is to perform a random search "around" a good point, but only if the current point is worse than average, i.e. if $\frac{f(x_i)}{N}>C$. In that case, 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).

@@ Line 1: / Line 1: @@
 == Basic local search (l0) ==
-The idea is just to perform a random search "around" a good point. So the process is the following:
+The idea is to perform a random search "around" a good point, but only if the current point is worse than average, i.e. if $C>\frac{f(x_i)}{N}$. In that case, 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).

@@ Line 1: / Line 1: @@
 == Basic local search (l0) ==
-The idea is just to perform a random search "around" the current best point. So the process is the following:
+The idea is just to perform a random search "around" a good 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).

@@ Line 12: / Line 12: @@
 === 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 $r=\rho*rand(0,1)^{\frac{1/D}{}}$
+# 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 $r=\rho*rand(0,1)$.

@@ Line 12: / Line 12: @@
 === 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 $r=\rho*rand(0,1)^{\frac{1/D}}$
+# 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 $r=\rho*rand(0,1)$.

← Older revision	Revision as of 08:06, 22 September 2015
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← Older revision	Revision as of 08:01, 22 September 2015
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Local search - Revision history

Admin: Admin moved page Last Chance / Local search to Local search without leaving a redirect: revert

Admin: Admin moved page Local search to Last Chance / Local search without leaving a redirect

Editor: /* Basic local search (l0) */

Editor: /* Basic local search (l0) */

Editor: /* Basic local search (l0) */

Editor: /* Random choice */

Editor: /* Random choice */

Editor at 17:12, 28 June 2013

MClerc: Created page with "TO COMPLETE == Basic local search (l0) == Define a hypersphere around the best point of the three ones defined in the selection phase. Radius= maximum distance to the o..."

MClerc: Created page with "TO COMPLETE == Basic local search (l0) == Define a hypersphere around the best point of the three ones defined in the selection phase.
Radius= maximum distance to the o..."