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84 | 84 | Through simulation of the evolutionary operations <firstterm>recombination</firstterm>, |
85 | 85 | <firstterm>mutation</firstterm>, and |
86 | 86 | <firstterm>selection</firstterm> new generations of search points are found |
87 | | - that show a higher average fitness than their ancestors. |
| 87 | + that show a higher average fitness than their ancestors. <xref linkend="geqo-figure"/> |
| 88 | + illustrates these steps. |
88 | 89 | </para> |
89 | 90 |
|
| 91 | + <figure id="geqo-figure"> |
| 92 | + <title>Structure of a Genetic Algorithm</title> |
| 93 | + <mediaobject> |
| 94 | + <imageobject> |
| 95 | + <imagedata fileref="images/genetic-algorithm.svg" format="SVG" width="100%"/> |
| 96 | + </imageobject> |
| 97 | + </mediaobject> |
| 98 | + </figure> |
| 99 | + |
90 | 100 | <para> |
91 | 101 | According to the <systemitem class="resource">comp.ai.genetic</systemitem> <acronym>FAQ</acronym> it cannot be stressed too |
92 | 102 | strongly that a <acronym>GA</acronym> is not a pure random search for a solution to a |
93 | 103 | problem. A <acronym>GA</acronym> uses stochastic processes, but the result is distinctly |
94 | 104 | non-random (better than random). |
95 | 105 | </para> |
96 | 106 |
|
97 | | - <figure id="geqo-diagram"> |
98 | | - <title>Structured Diagram of a Genetic Algorithm</title> |
99 | | - |
100 | | - <informaltable frame="none"> |
101 | | - <tgroup cols="2"> |
102 | | - <tbody> |
103 | | - <row> |
104 | | - <entry>P(t)</entry> |
105 | | - <entry>generation of ancestors at a time t</entry> |
106 | | - </row> |
107 | | - |
108 | | - <row> |
109 | | - <entry>P''(t)</entry> |
110 | | - <entry>generation of descendants at a time t</entry> |
111 | | - </row> |
112 | | - </tbody> |
113 | | - </tgroup> |
114 | | - </informaltable> |
115 | | - |
116 | | -<literallayout class="monospaced"> |
117 | | -+=========================================+ |
118 | | -|>>>>>>>>>>> Algorithm GA <<<<<<<<<<<<<<| |
119 | | -+=========================================+ |
120 | | -| INITIALIZE t := 0 | |
121 | | -+=========================================+ |
122 | | -| INITIALIZE P(t) | |
123 | | -+=========================================+ |
124 | | -| evaluate FITNESS of P(t) | |
125 | | -+=========================================+ |
126 | | -| while not STOPPING CRITERION do | |
127 | | -| +-------------------------------------+ |
128 | | -| | P'(t) := RECOMBINATION{P(t)} | |
129 | | -| +-------------------------------------+ |
130 | | -| | P''(t) := MUTATION{P'(t)} | |
131 | | -| +-------------------------------------+ |
132 | | -| | P(t+1) := SELECTION{P''(t) + P(t)} | |
133 | | -| +-------------------------------------+ |
134 | | -| | evaluate FITNESS of P''(t) | |
135 | | -| +-------------------------------------+ |
136 | | -| | t := t + 1 | |
137 | | -+===+=====================================+ |
138 | | -</literallayout> |
139 | | - </figure> |
140 | 107 | </sect1> |
141 | 108 |
|
142 | 109 | <sect1 id="geqo-pg-intro"> |
|
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