Trim trailing whitespace

[postgresql] / doc / src / sgml / geqo.sgml

diff --git a/doc/src/sgml/geqo.sgml b/doc/src/sgml/geqo.sgml
index 2f680762c13bb45c3b85bbba5c43011de112eb4b..e0f8adcd6ed6901653f9fb01196b221af92ee05d 100644 (file)
--- a/doc/src/sgml/geqo.sgml
+++ b/doc/src/sgml/geqo.sgml
@@ -1,31 +1,7 @@
-<!-- $PostgreSQL: pgsql/doc/src/sgml/geqo.sgml,v 1.40 2007/07/21 04:02:41 tgl Exp $ -->
+<!-- doc/src/sgml/geqo.sgml -->
 
  <chapter id="geqo">
-  <chapterinfo>
-   <author>
-    <firstname>Martin</firstname>
-    <surname>Utesch</surname>
-    <affiliation>
-     <orgname>
-      University of Mining and Technology
-     </orgname>
-     <orgdiv>
-      Institute of Automatic Control
-     </orgdiv>
-     <address>
-      <city>
-       Freiberg
-      </city>
-      <country>
-       Germany
-      </country>
-     </address>
-    </affiliation>
-   </author>
-   <date>1997-10-02</date>
-  </chapterinfo>
-
-  <title id="geqo-title">Genetic Query Optimizer</title>
+  <title>Genetic Query Optimizer</title>
 
   <para>
    <note>
@@ -49,7 +25,7 @@
     methods</firstterm> (e.g., nested loop, hash join, merge join in
     <productname>PostgreSQL</productname>) to process individual joins
     and a diversity of <firstterm>indexes</firstterm> (e.g.,
-    B-tree, hash, GiST and GIN in <productname>PostgreSQL</productname>) as 
+    B-tree, hash, GiST and GIN in <productname>PostgreSQL</productname>) as
     access paths for relations.
    </para>
 
@@ -88,8 +64,7 @@
 
    <para>
     The genetic algorithm (<acronym>GA</acronym>) is a heuristic optimization method which
-    operates through
-    nondeterministic, randomized search. The set of possible solutions for the
+    operates through randomized search. The set of possible solutions for the
     optimization problem is considered as a
     <firstterm>population</firstterm> of <firstterm>individuals</firstterm>.
     The degree of adaptation of an individual to its environment is specified
@@ -116,7 +91,7 @@
     According to the <systemitem class="resource">comp.ai.genetic</> <acronym>FAQ</acronym> it cannot be stressed too
     strongly that a <acronym>GA</acronym> is not a pure random search for a solution to a
     problem. A <acronym>GA</acronym> uses stochastic processes, but the result is distinctly
-    non-random (better than random). 
+    non-random (better than random).
    </para>
 
    <figure id="geqo-diagram">
@@ -260,9 +235,13 @@
    <para>
     This process is inherently nondeterministic, because of the randomized
     choices made during both the initial population selection and subsequent
-    <quote>mutation</> of the best candidates.  Hence different plans may
-    be selected from one run to the next, resulting in varying run time
-    and varying output row order.
+    <quote>mutation</> of the best candidates.  To avoid surprising changes
+    of the selected plan, each run of the GEQO algorithm restarts its
+    random number generator with the current <xref linkend="guc-geqo-seed">
+    parameter setting.  As long as <varname>geqo_seed</> and the other
+    GEQO parameters are kept fixed, the same plan will be generated for a
+    given query (and other planner inputs such as statistics).  To experiment
+    with different search paths, try changing <varname>geqo_seed</>.
    </para>
 
   </sect2>
@@ -325,12 +304,12 @@
    <itemizedlist>
     <listitem>
      <para>
-      <ulink url="http://www.cs.bham.ac.uk/Mirrors/ftp.de.uu.net/EC/clife/www/location.htm">
+      <ulink url="http://www.aip.de/~ast/EvolCompFAQ/">
       The Hitch-Hiker's Guide to Evolutionary Computation</ulink>, (FAQ for <ulink
       url="news://comp.ai.genetic"></ulink>)
      </para>
     </listitem>
-   
+
     <listitem>
      <para>
       <ulink url="http://www.red3d.com/cwr/evolve.html">