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348 lines
11 KiB
Plaintext
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$Header: /cvsroot/pgsql/doc/src/sgml/geqo.sgml,v 1.15 2000/12/22 21:51:57 petere Exp $
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Genetic Optimizer
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-->
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<chapter id="geqo">
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<docinfo>
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<author>
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<firstname>Martin</firstname>
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<surname>Utesch</surname>
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<affiliation>
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<orgname>
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University of Mining and Technology
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</orgname>
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<orgdiv>
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Institute of Automatic Control
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</orgdiv>
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<address>
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<city>
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Freiberg
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</city>
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<country>
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Germany
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</country>
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</address>
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</affiliation>
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</author>
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<date>1997-10-02</date>
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</docinfo>
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<title>Genetic Query Optimization in Database Systems</title>
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<para>
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<note>
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<title>Author</title>
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<para>
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Written by Martin Utesch (<email>utesch@aut.tu-freiberg.de</email>)
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for the Institute of Automatic Control at the University of Mining and Technology in Freiberg, Germany.
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</para>
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</note>
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</para>
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<sect1 id="geqo-intro">
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<title>Query Handling as a Complex Optimization Problem</title>
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<para>
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Among all relational operators the most difficult one to process and
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optimize is the <firstterm>join</firstterm>. The number of alternative plans to answer a query
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grows exponentially with the number of <command>join</command>s included in it. Further
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optimization effort is caused by the support of a variety of
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<firstterm>join methods</firstterm>
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(e.g., nested loop, hash join, merge join in <productname>Postgres</productname>) to
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process individual <command>join</command>s and a diversity of
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<firstterm>indices</firstterm> (e.g., r-tree,
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b-tree, hash in <productname>Postgres</productname>) as access paths for relations.
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</para>
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<para>
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The current <productname>Postgres</productname> optimizer
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implementation performs a <firstterm>near-exhaustive search</firstterm>
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over the space of alternative strategies. This query
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optimization technique is inadequate to support database application
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domains that involve the need for extensive queries, such as artificial
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intelligence.
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</para>
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<para>
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The Institute of Automatic Control at the University of Mining and
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Technology, in Freiberg, Germany, encountered the described problems as its
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folks wanted to take the <productname>Postgres</productname> DBMS as the backend for a decision
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support knowledge based system for the maintenance of an electrical
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power grid. The DBMS needed to handle large <command>join</command> queries for the
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inference machine of the knowledge based system.
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</para>
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<para>
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Performance difficulties in exploring the space of possible query
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plans created the demand for a new optimization technique being developed.
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</para>
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<para>
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In the following we propose the implementation of a <firstterm>Genetic Algorithm</firstterm>
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as an option for the database query optimization problem.
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</para>
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</sect1>
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<sect1 id="geqo-intro2">
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<title>Genetic Algorithms (<acronym>GA</acronym>)</title>
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<para>
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The <acronym>GA</acronym> is a heuristic optimization method which
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operates through
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determined, randomized search. The set of possible solutions for the
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optimization problem is considered as a
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<firstterm>population</firstterm> of <firstterm>individuals</firstterm>.
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The degree of adaption of an individual to its environment is specified
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by its <firstterm>fitness</firstterm>.
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</para>
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<para>
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The coordinates of an individual in the search space are represented
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by <firstterm>chromosomes</firstterm>, in essence a set of character
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strings. A <firstterm>gene</firstterm> is a
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subsection of a chromosome which encodes the value of a single parameter
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being optimized. Typical encodings for a gene could be <firstterm>binary</firstterm> or
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<firstterm>integer</firstterm>.
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</para>
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<para>
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Through simulation of the evolutionary operations <firstterm>recombination</firstterm>,
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<firstterm>mutation</firstterm>, and
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<firstterm>selection</firstterm> new generations of search points are found
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that show a higher average fitness than their ancestors.
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</para>
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<para>
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According to the "comp.ai.genetic" <acronym>FAQ</acronym> it cannot be stressed too
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strongly that a <acronym>GA</acronym> is not a pure random search for a solution to a
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problem. A <acronym>GA</acronym> uses stochastic processes, but the result is distinctly
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non-random (better than random).
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<programlisting>
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Structured Diagram of a <acronym>GA</acronym>:
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---------------------------
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P(t) generation of ancestors at a time t
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P''(t) generation of descendants at a time t
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+=========================================+
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|>>>>>>>>>>> Algorithm GA <<<<<<<<<<<<<<|
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+=========================================+
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| INITIALIZE t := 0 |
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+=========================================+
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| INITIALIZE P(t) |
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+=========================================+
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| evalute FITNESS of P(t) |
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+=========================================+
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| while not STOPPING CRITERION do |
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| +-------------------------------------+
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| | P'(t) := RECOMBINATION{P(t)} |
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| +-------------------------------------+
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| | P''(t) := MUTATION{P'(t)} |
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| +-------------------------------------+
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| | P(t+1) := SELECTION{P''(t) + P(t)} |
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| +-------------------------------------+
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| | evalute FITNESS of P''(t) |
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| +-------------------------------------+
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| | t := t + 1 |
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+===+=====================================+
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</programlisting>
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</para>
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</sect1>
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<sect1 id="geqo-pg-intro">
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<title>Genetic Query Optimization (<acronym>GEQO</acronym>) in Postgres</title>
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<para>
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The <acronym>GEQO</acronym> module is intended for the solution of the query
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optimization problem similar to a traveling salesman problem (<acronym>TSP</acronym>).
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Possible query plans are encoded as integer strings. Each string
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represents the <command>join</command> order from one relation of the query to the next.
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E. g., the query tree
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<programlisting>
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/\
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/\ 2
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/\ 3
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4 1
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</programlisting>
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is encoded by the integer string '4-1-3-2',
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which means, first join relation '4' and '1', then '3', and
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then '2', where 1, 2, 3, 4 are relids within the
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<productname>Postgres</productname> optimizer.
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</para>
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<para>
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Parts of the <acronym>GEQO</acronym> module are adapted from D. Whitley's Genitor
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algorithm.
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</para>
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<para>
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Specific characteristics of the <acronym>GEQO</acronym>
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implementation in <productname>Postgres</productname>
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are:
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<itemizedlist spacing="compact" mark="bullet">
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<listitem>
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<para>
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Usage of a <firstterm>steady state</firstterm> <acronym>GA</acronym> (replacement of the least fit
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individuals in a population, not whole-generational replacement)
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allows fast convergence towards improved query plans. This is
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essential for query handling with reasonable time;
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</para>
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</listitem>
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<listitem>
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<para>
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Usage of <firstterm>edge recombination crossover</firstterm> which is
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especially suited
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to keep edge losses low for the solution of the
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<acronym>TSP</acronym> by means of a <acronym>GA</acronym>;
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</para>
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</listitem>
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<listitem>
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<para>
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Mutation as genetic operator is deprecated so that no repair
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mechanisms are needed to generate legal <acronym>TSP</acronym> tours.
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</para>
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</listitem>
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</itemizedlist>
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</para>
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<para>
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The <acronym>GEQO</acronym> module allows
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the <productname>Postgres</productname> query optimizer to
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support large <command>join</command> queries effectively through
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non-exhaustive search.
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</para>
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<sect2 id="geqo-future">
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<title>Future Implementation Tasks for
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<productname>PostgreSQL</> <acronym>GEQO</acronym></title>
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<para>
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Work is still needed to improve the genetic algorithm parameter
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settings.
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In file <filename>backend/optimizer/geqo/geqo_params.c</filename>, routines
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<function>gimme_pool_size</function> and <function>gimme_number_generations</function>,
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we have to find a compromise for the parameter settings
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to satisfy two competing demands:
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<itemizedlist spacing="compact">
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<listitem>
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<para>
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Optimality of the query plan
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</para>
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</listitem>
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<listitem>
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<para>
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Computing time
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</para>
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</listitem>
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</itemizedlist>
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</para>
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</sect2>
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<bibliography id="geqo-biblio">
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<title>
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References
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</title>
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<para>Reference information for <acronym>GEQ</acronym> algorithms.
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</para>
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<biblioentry>
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<bookbiblio>
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<title>
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The Hitch-Hiker's Guide to Evolutionary Computation
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</title>
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<authorgroup>
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<author>
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<firstname>Jörg</firstname>
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<surname>Heitkötter</surname>
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</author>
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<author>
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<firstname>David</firstname>
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<surname>Beasley</surname>
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</author>
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</authorgroup>
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<publisher>
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<publishername>
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InterNet resource
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</publishername>
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</publisher>
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<abstract>
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<para>
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FAQ in <ulink url="news://comp.ai.genetic">comp.ai.genetic</ulink>
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is available at <ulink
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url="ftp://ftp.Germany.EU.net/pub/research/softcomp/EC/Welcome.html">Encore</ulink>.
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</para>
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</abstract>
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</bookbiblio>
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<bookbiblio>
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<title>
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The Design and Implementation of the Postgres Query Optimizer
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</title>
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<authorgroup>
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<author>
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<firstname>Z.</firstname>
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<surname>Fong</surname>
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</author>
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</authorgroup>
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<publisher>
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<publishername>
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University of California, Berkeley Computer Science Department
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</publishername>
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</publisher>
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<abstract>
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<para>
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File <filename>planner/Report.ps</filename> in the 'postgres-papers' distribution.
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</para>
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</abstract>
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</bookbiblio>
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<bookbiblio>
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<title>
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Fundamentals of Database Systems
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</title>
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<authorgroup>
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<author>
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<firstname>R.</firstname>
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<surname>Elmasri</surname>
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</author>
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<author>
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<firstname>S.</firstname>
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<surname>Navathe</surname>
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</author>
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</authorgroup>
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<publisher>
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<publishername>
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The Benjamin/Cummings Pub., Inc.
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</publishername>
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</publisher>
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</bookbiblio>
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</biblioentry>
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</bibliography>
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</sect1>
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</chapter>
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