test_results-aix.txt

在GNU标准下开发的遗传算法程序包

./randtest: Random Number Test
Using the RAN2 random number generator (RNG).
Using specified random seed 555
Library thinks the random seed is 555

running the chi-square test for randomness of the RNG...
 (there will be some failures of the chi-square test)
  integer test (1000):  chi-squared should be within 20 of 100
    run #0	93.2	
    run #1	101.52	
    run #2	119.752	
    run #3	103.175	
    run #4	130.718	***failed***
    run #5	118.672	
    run #6	93.8672	
    run #7	101.787	
    run #8	132.579	***failed***
    run #9	114.258	
  integer test (10000):  chi-squared should be within 20 of 100
    run #0	88.2	
    run #1	87.82	
    run #2	77.3	***failed***
    run #3	81.22	
    run #4	94.18	
    run #5	113.46	
    run #6	102.72	
    run #7	94.24	
    run #8	71.24	***failed***
    run #9	117.86	
  integer test (10000):  chi-squared should be within 20 of 100
    run #0	109.12	
    run #1	113.06	
    run #2	133.26	***failed***
    run #3	99.82	
    run #4	109.18	
    run #5	90.52	
    run #6	100.58	
    run #7	101.34	
    run #8	93.74	
    run #9	89.38	
  integer test (100000):  chi-squared should be within 20 of 100
    run #0	119.2	
    run #1	99.978	
    run #2	110.9	
    run #3	103.678	
    run #4	82.366	
    run #5	102.464	
    run #6	91.632	
    run #7	86.788	
    run #8	109.812	
    run #9	86.968	
Example 1

This program tries to fill a 2DBinaryStringGenome with
alternating 1s and 0s using a SimpleGA

The GA found:
0101010101
1010101010
0101010101
1010101010
0101010101

Example 2

This program generates a sequence of random numbers then uses
a simple GA and binary-to-decimal genome to match the
sequence.

input sequence:
  0.265886    54.6261          3   -2.85019    69151.2 0.00150462          2 
random values in the genome:
  0.980392    18.4314          3   -4.34118    23997.6 0.00276471   0.494118 
the ga generated:
  0.278431    54.5098          3   -2.87059    2450.59 0.00163529    2.00392 

best of generation data are in 'bog.dat'
Example 3

This program reads in a data file then runs a simple GA whose
objective function tries to match the pattern of bits that are
in the data file.

input pattern:
                                
          * * * * * *           
      * *             * *       
    *                     *     
    *     * *     * *     *     
  *       * *     * *       *   
  *                         *   
  *                         *   
  *                         *   
    *   *             *   *     
    *     * * * * * *     *     
      * *             * *       
          * * * * * *           
                                

best of generation data are in 'bog.dat'
the ga generated:
  *       *                     
          * * * * *             
      * *   *         * *       
    *                 *   *     
    *     * * *   * *     *     
  *               * *   *   *   
  *               *         *   
  * *           *     * *     * 
  *       *           *     *   
    *         *       *   *   * 
*         * * * * * *   * *     
      * *                       
          * * * * * * *         
              *     *           

Example 4

This program tries to fill a 3DBinaryStringGenome with
alternating 1s and 0s using a SteadyStateGA

the ga generated:
0101010101
1010101010
0101010101
1010101010
0101010101

1010101010
0101010101
1010101010
0101010101
1010101010

0101010101
1010101010
0101010101
1010101010
0101010101


best of generation data are in 'bog.dat'
Example 5

This program shows how to use a composite genome.  It reads
a matrix from a data file and a set of values to be matched in
a binary-to-decimal genome then uses a steady-state GA to
match the pattern and value set.

input pattern:
                                
          * * * * * *           
      * *             * *       
    *                     *     
    *     * *     * *     *     
  *       * *     * *       *   
  *                         *   
  *                         *   
  *                         *   
    *   *             *   *     
    *     * * * * * *     *     
      * *             * *       
          * * * * * *           
                                

input sequence:
10.2 32.5 66 99.234 0.003 210 

the ga generated:
                                
          * * * * * *           
      * *             * *       
    *                     *     
    *     * *     * *     *     
  *       * *     * *       *   
  *                         *   
  *                         *   
  *                         *   
    *   *             *   *     
    *     * * * * * *     *     
      * *             * *       
          * * * * * *           
                                

8.3767 32.4286 55.2418 101.924 0.00209011 150.615 

Example 6

This example uses a SteadyState GA and Tree<int> genome.  It
tries to maximize the size of the tree genomes that it
contains.  The genomes contain ints in its nodes.

8653 nodes, 120 levels deep.
best of generation data are in 'bog.dat'
Example 7

This program reads in a data file then runs a steady-state GA 
whose objective function tries to match the pattern of bits that
are in the data file.

input pattern:
                                
          * * * * * *           
      * *             * *       
    *                     *     
    *     * *     * *     *     
  *       * *     * *       *   
  *                         *   
  *                         *   
  *                         *   
    *   *             *   *     
    *     * * * * * *     *     
      * *             * *       
          * * * * * *           
                                

the ga generated:
        *             *         
          * * * * *             
      * *           * * *       
    *                     *     
    *     * *     * *     *     
  *       * *     * *       *   
  *                         *   
  *                         *   
  *                         *   
    *   *             *   *     
*   *     * * * * * * *   *     
      * *             * *       
          * * * * * *     *     
                                

best of generation data are in 'bog.dat'
Example 8

This program runs a steady-state GA whose objective function
tries to maximize the size of the list and tries to make lists
that contain the number 101 in the nodes.  The lists contain
ints in the nodes.

the list contains 0 nodes
the ga used the parameters:
minimaxi	1
number_of_generations	50
generations_to_convergence	20
convergence_percentage	0.99
crossover_probability	0.6
mutation_probability	0.05
population_size	40
score_frequency	1
flush_frequency	10
record_diversity	0
score_filename	bog.dat
select_scores	7
number_of_best	1
replacement_percentage	0.5
replacement_number	20

Example 9

This program finds the maximum value in the function
  y = - x1^2 - x2^2
with the constraints
     -5 <= x1 <= 5
     -5 <= x2 <= 5


the ga found an optimum at the point (7.62951e-05, 0.00328069)

best of generation data are in 'bog.dat'
Example 10

This program uses sharing to do speciation.  The objective
function has more than one optimum, so different genomes
may have equally high scores.  Speciation keeps the population
from clustering at one optimum.
  Both gene-wise and phenotype-wise distance functions are used.
  Populations from all three runs are written to the files 
pop.nospec.dat, pop.genespec.dat and pop.phenespec.dat.  The
function is written to the file sinusoid.dat

running with no speciation (fitness proportionate scaling)...
the ga found an optimum at the point 3.2549
running the ga with speciation (sharing using bit-wise)...
the ga found an optimum at the point 2.2549
running the ga with speciation (sharing using phenotype-wise)...
the ga found an optimum at the point 4.2549
dumping the function to file...
Example 11

This program illustrates the use of order-based lists.  The
list in this problem contains 25 numbers, 0 to 24.  It tries
to put them in descending order from 24 to 0.

the ga generated the following list (objective score is 24):
24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 
best of generation data are in 'bog.dat'
minimaxi	1
number_of_generations	1000
generations_to_convergence	20
convergence_percentage	0.99
crossover_probability	0.6
mutation_probability	0.01
population_size	30
score_frequency	10
flush_frequency	10
record_diversity	0
score_filename	bog.dat
select_scores	2
number_of_best	1
replacement_percentage	0.5
replacement_number	15

Example 12

This program illustrates the use of order-based strings.  The
string in this problem contains 26 letters, a to z.  It tries
to put them in alphabetic order.

the ga generated the following string (objective score is 26):
abcdefghijklmnopqrstuvwxyz
GAStringGenome
Example 13

This program runs a GA-within-GA.  The outer level GA tries to
match the pattern read in from a file.  The inner level GA is
run only when the outer GA reaches a threshhold objective score
then it tries to match a sequence of numbers that were generated
randomly at the beginning of the program's execution.

You might have to run the primary GA for more than 5000
generations to get a score high enough to kick in the secondary
genetic algorithm.  Use the ngen option to do this on the
command line.

input pattern:
                                
          * * * * * *           
      * *             * *       
    *                     *     
    *     * *     * *     *     
  *       * *     * *       *   
  *                         *   
  *                         *   
  *                         *   
    *   *             *   *     
    *     * * * * * *     *     
      * *             * *       
          * * * * * *           
                                

input sequence:
  0.265886    54.6261          3   -2.85019    69151.2 0.00150462          2 
the ga generated:
  *   *       *     *       * * 
        * *     * *           * 
      *               *         
    * * * *             * *     
  * * * *   *     *       *     
  *       * * * * *         *   
          *   *   *     *   *   
        *   *     *   *   * * * 
  *       *     *       * * * * 
* * *   *     *   * *   * *   * 
*   * *   * *     * * *   *     
    * * *                 *   * 
    * *       *   * *     *     
    * *           * *           

  0.411765    12.9412          3   -2.16471    74143.5 0.00749412    6.17647 

best of generation data are in 'bog.dat'
Example 14

This example shows how to create a genome that contains
a list of lists.  We create a composite genome that has
lists in it.  Each list has some nodes, only one of which
contains the number 0.  The objective is to move the node with
number 0 in it to the nth position where n is the number of the
list within the composite genome.

a randomly-generated set of paths:
list 0:	28 20 21 22 25 24 0 26 23 27 
list 1:	22 20 23 21 0 24 25 26 28 27 
list 2:	20 23 21 28 27 24 22 26 0 25 
list 3:	0 20 21 22 23 24 27 26 25 28 
list 4:	0 28 21 22 23 24 27 26 20 25 
list 5:	28 27 21 26 23 24 25 22 20 0 

the ga generated:
list 0:	0 26 24 22 25 23 27 28 21 20 
list 1:	24 0 20 26 23 22 25 27 21 28 
list 2:	21 26 0 22 23 20 25 27 28 24 
list 3:	24 21 22 0 23 27 25 20 26 28 
list 4:	24 26 23 21 0 20 22 28 27 25 
list 5:	20 26 21 25 23 0 24 22 27 28 

Example 15

This program generates a sequence of random numbers then uses
a simple GA and binary-to-decimal genome to match the
sequence.  It uses the convergence of the best-of-generation
as the criterion for when to stop.

input sequence:
  0.265886    54.6261          3   -2.85019    69151.2 0.00150462          2 
random values in the genome:
  0.419608     85.098          3   -4.90588       6760 0.00834118    5.92941 
the ga generated:
  0.247059    49.8039          3   -2.74118    60823.5 0.00156471    2.00392 

Example 16

This example uses a SteadyState GA and Tree<int> genome.  It
tries to maximize the size of the tree genomes that it
contains.  The genomes contain points in its nodes.  Two
different runs are made:  first with the swap subtree mutator,