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>Chapter 8. Genetic Query Optimization</TD
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><H1
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><A
NAME="GEQO-INTRO2"
>8.2. Genetic Algorithms</A
></H1
><P
> The genetic algorithm (<SPAN
CLASS="ACRONYM"
>GA</SPAN
>) is a heuristic optimization method which
operates through
determined, 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
> <SPAN
CLASS="ACRONYM"
>FAQ</SPAN
> it cannot be stressed too
strongly that a <SPAN
CLASS="ACRONYM"
>GA</SPAN
> is not a pure random search for a solution to a
problem. A <SPAN
CLASS="ACRONYM"
>GA</SPAN
> uses stochastic processes, but the result is distinctly
non-random (better than random).
</P
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><A
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><P
><B
>Figure 8-1. Structured Diagram of a Genetic Algorithm</B
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>P(t)</TD
><TD
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VALIGN="TOP"
>generation of ancestors at a time t</TD
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>P''(t)</TD
><TD
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>generation of descendants at a time t</TD
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><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
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