| Server IP : 103.119.228.120 / Your IP : 3.14.134.18 Web Server : Apache System : Linux v8.techscape8.com 3.10.0-1160.119.1.el7.tuxcare.els2.x86_64 #1 SMP Mon Jul 15 12:09:18 UTC 2024 x86_64 User : nobody ( 99) PHP Version : 5.6.40 Disable Function : shell_exec,symlink,system,exec,proc_get_status,proc_nice,proc_terminate,define_syslog_variables,syslog,openlog,closelog,escapeshellcmd,passthru,ocinum cols,ini_alter,leak,listen,chgrp,apache_note,apache_setenv,debugger_on,debugger_off,ftp_exec,dl,dll,myshellexec,proc_open,socket_bind,proc_close,escapeshellarg,parse_ini_filepopen,fpassthru,exec,passthru,escapeshellarg,escapeshellcmd,proc_close,proc_open,ini_alter,popen,show_source,proc_nice,proc_terminate,proc_get_status,proc_close,pfsockopen,leak,apache_child_terminate,posix_kill,posix_mkfifo,posix_setpgid,posix_setsid,posix_setuid,dl,symlink,shell_exec,system,dl,passthru,escapeshellarg,escapeshellcmd,myshellexec,c99_buff_prepare,c99_sess_put,fpassthru,getdisfunc,fx29exec,fx29exec2,is_windows,disp_freespace,fx29sh_getupdate,fx29_buff_prepare,fx29_sess_put,fx29shexit,fx29fsearch,fx29ftpbrutecheck,fx29sh_tools,fx29sh_about,milw0rm,imagez,sh_name,myshellexec,checkproxyhost,dosyayicek,c99_buff_prepare,c99_sess_put,c99getsource,c99sh_getupdate,c99fsearch,c99shexit,view_perms,posix_getpwuid,posix_getgrgid,posix_kill,parse_perms,parsesort,view_perms_color,set_encoder_input,ls_setcheckboxall,ls_reverse_all,rsg_read,rsg_glob,selfURL,dispsecinfo,unix2DosTime,addFile,system,get_users,view_size,DirFiles,DirFilesWide,DirPrintHTMLHeaders,GetFilesTotal,GetTitles,GetTimeTotal,GetMatchesCount,GetFileMatchesCount,GetResultFiles,fs_copy_dir,fs_copy_obj,fs_move_dir,fs_move_obj,fs_rmdir,SearchText,getmicrotime MySQL : ON | cURL : ON | WGET : ON | Perl : ON | Python : ON | Sudo : ON | Pkexec : ON Directory : /usr/local/ssl/share/doc/postgresql-9.2.24/html/ |
Upload File : |
<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd">
<HTML
><HEAD
><TITLE
>Genetic Algorithms</TITLE
><META
NAME="GENERATOR"
CONTENT="Modular DocBook HTML Stylesheet Version 1.79"><LINK
REV="MADE"
HREF="mailto:pgsql-docs@postgresql.org"><LINK
REL="HOME"
TITLE="PostgreSQL 9.2.24 Documentation"
HREF="index.html"><LINK
REL="UP"
TITLE="Genetic Query Optimizer"
HREF="geqo.html"><LINK
REL="PREVIOUS"
TITLE="Query Handling as a Complex Optimization Problem"
HREF="geqo-intro.html"><LINK
REL="NEXT"
TITLE="Genetic Query Optimization (GEQO) in PostgreSQL"
HREF="geqo-pg-intro.html"><LINK
REL="STYLESHEET"
TYPE="text/css"
HREF="stylesheet.css"><META
HTTP-EQUIV="Content-Type"
CONTENT="text/html; charset=ISO-8859-1"><META
NAME="creation"
CONTENT="2017-11-06T22:43:11"></HEAD
><BODY
CLASS="SECT1"
><DIV
CLASS="NAVHEADER"
><TABLE
SUMMARY="Header navigation table"
WIDTH="100%"
BORDER="0"
CELLPADDING="0"
CELLSPACING="0"
><TR
><TH
COLSPAN="5"
ALIGN="center"
VALIGN="bottom"
><A
HREF="index.html"
>PostgreSQL 9.2.24 Documentation</A
></TH
></TR
><TR
><TD
WIDTH="10%"
ALIGN="left"
VALIGN="top"
><A
TITLE="Query Handling as a Complex Optimization Problem"
HREF="geqo-intro.html"
ACCESSKEY="P"
>Prev</A
></TD
><TD
WIDTH="10%"
ALIGN="left"
VALIGN="top"
><A
HREF="geqo.html"
ACCESSKEY="U"
>Up</A
></TD
><TD
WIDTH="60%"
ALIGN="center"
VALIGN="bottom"
>Chapter 51. Genetic Query Optimizer</TD
><TD
WIDTH="20%"
ALIGN="right"
VALIGN="top"
><A
TITLE="Genetic Query Optimization (GEQO) in PostgreSQL"
HREF="geqo-pg-intro.html"
ACCESSKEY="N"
>Next</A
></TD
></TR
></TABLE
><HR
ALIGN="LEFT"
WIDTH="100%"></DIV
><DIV
CLASS="SECT1"
><H1
CLASS="SECT1"
><A
NAME="GEQO-INTRO2"
>51.2. Genetic Algorithms</A
></H1
><P
> The genetic algorithm (<ACRONYM
CLASS="ACRONYM"
>GA</ACRONYM
>) is a heuristic optimization method which
operates through randomized search. The set of possible solutions for the
optimization problem is considered as a
<I
CLASS="FIRSTTERM"
>population</I
> of <I
CLASS="FIRSTTERM"
>individuals</I
>.
The degree of adaptation of an individual to its environment is specified
by its <I
CLASS="FIRSTTERM"
>fitness</I
>.
</P
><P
> The coordinates of an individual in the search space are represented
by <I
CLASS="FIRSTTERM"
>chromosomes</I
>, in essence a set of character
strings. A <I
CLASS="FIRSTTERM"
>gene</I
> is a
subsection of a chromosome which encodes the value of a single parameter
being optimized. Typical encodings for a gene could be <I
CLASS="FIRSTTERM"
>binary</I
> or
<I
CLASS="FIRSTTERM"
>integer</I
>.
</P
><P
> Through simulation of the evolutionary operations <I
CLASS="FIRSTTERM"
>recombination</I
>,
<I
CLASS="FIRSTTERM"
>mutation</I
>, and
<I
CLASS="FIRSTTERM"
>selection</I
> new generations of search points are found
that show a higher average fitness than their ancestors.
</P
><P
> According to the <SPAN
CLASS="SYSTEMITEM"
>comp.ai.genetic</SPAN
> <ACRONYM
CLASS="ACRONYM"
>FAQ</ACRONYM
> it cannot be stressed too
strongly that a <ACRONYM
CLASS="ACRONYM"
>GA</ACRONYM
> is not a pure random search for a solution to a
problem. A <ACRONYM
CLASS="ACRONYM"
>GA</ACRONYM
> uses stochastic processes, but the result is distinctly
non-random (better than random).
</P
><DIV
CLASS="FIGURE"
><A
NAME="GEQO-DIAGRAM"
></A
><P
><B
>Figure 51-1. Structured Diagram of a Genetic Algorithm</B
></P
><DIV
CLASS="INFORMALTABLE"
><P
></P
><A
NAME="AEN98333"
></A
><TABLE
BORDER="0"
FRAME="void"
CLASS="CALSTABLE"
><COL><COL><TBODY
><TR
><TD
>P(t)</TD
><TD
>generation of ancestors at a time t</TD
></TR
><TR
><TD
>P''(t)</TD
><TD
>generation of descendants at a time t</TD
></TR
></TBODY
></TABLE
><P
></P
></DIV
><PRE
CLASS="LITERALLAYOUT"
>+=========================================+
|>>>>>>>>>>> Algorithm GA <<<<<<<<<<<<<<|
+=========================================+
| INITIALIZE t := 0 |
+=========================================+
| INITIALIZE P(t) |
+=========================================+
| evaluate FITNESS of P(t) |
+=========================================+
| while not STOPPING CRITERION do |
| +-------------------------------------+
| | P'(t) := RECOMBINATION{P(t)} |
| +-------------------------------------+
| | P''(t) := MUTATION{P'(t)} |
| +-------------------------------------+
| | P(t+1) := SELECTION{P''(t) + P(t)} |
| +-------------------------------------+
| | evaluate FITNESS of P''(t) |
| +-------------------------------------+
| | t := t + 1 |
+===+=====================================+</PRE
></DIV
></DIV
><DIV
CLASS="NAVFOOTER"
><HR
ALIGN="LEFT"
WIDTH="100%"><TABLE
SUMMARY="Footer navigation table"
WIDTH="100%"
BORDER="0"
CELLPADDING="0"
CELLSPACING="0"
><TR
><TD
WIDTH="33%"
ALIGN="left"
VALIGN="top"
><A
HREF="geqo-intro.html"
ACCESSKEY="P"
>Prev</A
></TD
><TD
WIDTH="34%"
ALIGN="center"
VALIGN="top"
><A
HREF="index.html"
ACCESSKEY="H"
>Home</A
></TD
><TD
WIDTH="33%"
ALIGN="right"
VALIGN="top"
><A
HREF="geqo-pg-intro.html"
ACCESSKEY="N"
>Next</A
></TD
></TR
><TR
><TD
WIDTH="33%"
ALIGN="left"
VALIGN="top"
>Query Handling as a Complex Optimization Problem</TD
><TD
WIDTH="34%"
ALIGN="center"
VALIGN="top"
><A
HREF="geqo.html"
ACCESSKEY="U"
>Up</A
></TD
><TD
WIDTH="33%"
ALIGN="right"
VALIGN="top"
>Genetic Query Optimization (<ACRONYM
CLASS="ACRONYM"
>GEQO</ACRONYM
>) in PostgreSQL</TD
></TR
></TABLE
></DIV
></BODY
></HTML
>