randtest: Random Number TestUsing the RAN2 random number generator (RNG).Using specified random seed 555Library thinks the random seed is 555running 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 1This program tries to fill a 2DBinaryStringGenome withalternating 1s and 0s using a SimpleGAThe GA found:01010101011010101010010101010110101010100101010101Example 2This program generates a sequence of random numbers then usesa simple GA and binary-to-decimal genome to match thesequence.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 3This program reads in a data file then runs a simple GA whoseobjective function tries to match the pattern of bits that arein the data file.input pattern:                                          * * * * * *                 * *             * *           *                     *         *     * *     * *     *       *       * *     * *       *     *                         *     *                         *     *                         *       *   *             *   *         *     * * * * * *     *           * *             * *                 * * * * * *                                           best of generation data are in 'bog.dat'the ga generated:  *       *                               * * * * *                   * *   *         * *           *                 *   *         *     * * *   * *     *       *               * *   *   *     *               *         *     * *           *     * *     *   *       *           *     *       *         *       *   *   * *         * * * * * *   * *           * *                                 * * * * * * *                       *     *           Example 4This program tries to fill a 3DBinaryStringGenome withalternating 1s and 0s using a SteadyStateGAthe ga generated:010101010110101010100101010101101010101001010101011010101010010101010110101010100101010101101010101001010101011010101010010101010110101010100101010101best of generation data are in 'bog.dat'Example 5This program shows how to use a composite genome.  It readsa matrix from a data file and a set of values to be matched ina binary-to-decimal genome then uses a steady-state GA tomatch the pattern and value set.input pattern:                                          * * * * * *                 * *             * *           *                     *         *     * *     * *     *       *       * *     * *       *     *                         *     *                         *     *                         *       *   *             *   *         *     * * * * * *     *           * *             * *                 * * * * * *                                           input sequence:10.2 32.5 66 99.234 0.003 210 the ga generated:                                          * * * * * *                 * *             * *           *                     *         *     * *     * *     *       *       * *     * *       *     *                         *     *                         *     *                         *       *   *             *   *         *     * * * * * *     * *         * *             * *                 * * * * * *                                           10.2262 35.4048 74.9209 98.9796 0.00226374 323.692 Example 6This example uses a SteadyState GA and Tree<int> genome.  Ittries to maximize the size of the tree genomes that itcontains.  The genomes contain ints in its nodes.8653 nodes, 120 levels deep.best of generation data are in 'bog.dat'Example 7This program reads in a data file then runs a steady-state GA whose objective function tries to match the pattern of bits thatare in the data file.input pattern:                                          * * * * * *                 * *             * *           *                     *         *     * *     * *     *       *       * *     * *       *     *                         *     *                         *     *                         *       *   *             *   *         *     * * * * * *     *           * *             * *                 * * * * * *                                           the ga generated:        *             *                   * * * * *                   * *           * * *           *                     *         *     * *     * *     *       *       * *     * *       *     *                         *     *                         *     *                         *       *   *             *   *     *   *     * * * * * * *   *           * *             * *                 * * * * * *     *                                     best of generation data are in 'bog.dat'Example 8This program runs a steady-state GA whose objective functiontries to maximize the size of the list and tries to make liststhat contain the number 101 in the nodes.  The lists containints in the nodes.the list contains 0 nodesthe ga used the parameters:minimaxi	1number_of_generations	50generations_to_convergence	20convergence_percentage	0.99crossover_probability	0.6mutation_probability	0.05population_size	40score_frequency	1flush_frequency	10record_diversity	0score_filename	bog.datselect_scores	7number_of_best	1replacement_percentage	0.5replacement_number	20Example 9This program finds the maximum value in the function  y = - x1^2 - x2^2with the constraints     -5 <= x1 <= 5     -5 <= x2 <= 5the ga found an optimum at the point (7.62939e-05, 0.00328064)best of generation data are in 'bog.dat'Example 10This program uses sharing to do speciation.  The objectivefunction has more than one optimum, so different genomesmay have equally high scores.  Speciation keeps the populationfrom 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.  Thefunction is written to the file sinusoid.datrunning with no speciation (fitness proportionate scaling)...the ga found an optimum at the point 3.2549running the ga with speciation (sharing using bit-wise)...the ga found an optimum at the point 2.2549running the ga with speciation (sharing using phenotype-wise)...the ga found an optimum at the point 4.2549dumping the function to file...Example 11This program illustrates the use of order-based lists.  Thelist in this problem contains 25 numbers, 0 to 24.  It triesto 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	1number_of_generations	1000generations_to_convergence	20convergence_percentage	0.99crossover_probability	0.6mutation_probability	0.01population_size	30score_frequency	10flush_frequency	10record_diversity	0score_filename	bog.datselect_scores	2number_of_best	1replacement_percentage	0.5replacement_number	15Example 12This program illustrates the use of order-based strings.  Thestring in this problem contains 26 letters, a to z.  It triesto put them in alphabetic order.the ga generated the following string (objective score is 26):abcdefghijklmnopqrstuvwxyzGAStringGenomeExample 13This program runs a GA-within-GA.  The outer level GA tries tomatch the pattern read in from a file.  The inner level GA isrun only when the outer GA reaches a threshhold objective scorethen it tries to match a sequence of numbers that were generatedrandomly at the beginning of the program's execution.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 14This example shows how to create a genome that containsa list of lists.  We create a composite genome that haslists in it.  Each list has some nodes, only one of whichcontains the number 0.  The objective is to move the node withnumber 0 in it to the nth position where n is the number of thelist 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 15This program generates a sequence of random numbers then usesa simple GA and binary-to-decimal genome to match thesequence.  It uses the convergence of the best-of-generationas 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 16This example uses a SteadyState GA and Tree<int> genome.  Ittries to maximize the size of the tree genomes that itcontains.  The genomes contain points in its nodes.  Twodifferent runs are made:  first with the swap subtree mutator,