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source: branches/OKBJavaConnector/ECJClient/src/ec/vector/breed/GeneDuplicationPipeline.java @ 6152

Last change on this file since 6152 was 6152, checked in by bfarka, 13 years ago

added ecj and custom statistics to communicate with the okb services #1441

File size: 4.2 KB
Line 
1/*
2  Copyright 2010 by Sean Luke and George Mason University
3  Licensed under the Academic Free License version 3.0
4  See the file "LICENSE" for more information
5*/
6
7package ec.vector.breed;
8
9import ec.BreedingPipeline;
10import ec.EvolutionState;
11import ec.Individual;
12import ec.SelectionMethod;
13import ec.util.Parameter;
14import ec.vector.*;
15
16/**
17 * <p>GeneDuplicationPipeline is designed to duplicate a sequence of genes from the chromosome and append
18 * them to the end of the chromosome.  The sequence of genes copied are randomly determined.  That is to
19 * say a random begining index is selected and a random ending index is selected from the chromosome.  Then
20 * this area is then copied (begining inclusive, ending exclusive) and appended to the end of the chromosome.
21 * Since randomness is a factor several checks are performed to make sure the begining and ending indicies are
22 * valid.  For example, since the ending index is exclusive, the ending index cannot equal the begining index (a
23 * new ending index would be randomly seleceted in this case).  Likewise the begining index cannot be larger than the
24 * ending index (they would be swapped in this case).</p>
25 *
26 * <p><b>Default Base</b><br>
27 * ec.vector.breed.GeneDuplicationPipeline
28 *
29 * @author Sean Luke, Joseph Zelibor III, and Eric Kangas
30 * @version 1.0
31 */
32public class GeneDuplicationPipeline extends BreedingPipeline
33    {
34    public static final String P_DUPLICATION = "duplicate";
35    public static final int NUM_SOURCES = 1;
36
37    public Parameter defaultBase()
38        {
39        return VectorDefaults.base().push(P_DUPLICATION);
40        }
41
42    public int numSources() { return NUM_SOURCES; }
43
44    public int produce(int min,
45        int max,
46        int start,
47        int subpopulation,
48        Individual[] inds,
49        EvolutionState state,
50        int thread)
51        {
52
53        // grab individuals from our source and stick 'em right into inds.
54        // we'll modify them from there
55        int n = sources[0].produce(min,max,start,subpopulation,inds,state,thread);
56
57
58        // should we bother?
59        if (!state.random[thread].nextBoolean(likelihood))
60            return reproduce(n, start, subpopulation, inds, state, thread, false);  // DON'T produce children from source -- we already did
61
62
63        // now let's mutate 'em
64        for(int q=start; q < n+start; q++)
65            {
66            if (sources[0] instanceof SelectionMethod)
67                inds[q] = (Individual)(inds[q].clone());
68
69            //duplicate from the genome between a random begin and end point,
70            //and put that at the end of the new genome.
71            VectorIndividual ind = (VectorIndividual)(inds[q]);
72           
73            int len = ind.genomeLength();
74
75            //zero length individual, just return
76            if (len == 0)
77                {
78                return n;
79                }
80
81            int end = 0;
82            int begin = state.random[thread].nextInt(len+1);
83            do
84                {
85                end = state.random[thread].nextInt(len+1);
86                }
87            while (begin == end);  //because the end is exclusive, start cannot be
88            //equal to end.
89           
90
91            if (end < begin)
92                {
93                int temp = end;  //swap if necessary
94                end = begin;
95                begin = temp;
96                }
97
98            // copy the original into a new array.
99            Object[] original = new Object[2];
100            ind.split(new int[] {0, len}, original);
101                       
102            // copy the splice into a new array
103            Object[] splice = new Object[3];
104            ind.split(new int[] {begin, end}, splice);
105                       
106            // clone the genes in splice[1] (which we'll concatenate back in) in case we're using GeneVectorIndividual
107            ind.cloneGenes(splice[1]);
108           
109            // appends the pieces together with the splice at the end.
110            ind.join(new Object[] {original[1], splice[1]});
111            }
112        return n;  // number of individuals produced, 1 here.
113        }
114
115    }
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