second with the destructive mutator.initializing...evolving for 10 generations.............the ga generated a tree with 37 nodes, 9 levels deep.initializing...evolving for 10 generations.............the ga generated a tree with 35 nodes, 12 levels deep.Example 17This program illustrates the use of a 2DArrayGenome withthree alleles.  It tries to fill a 2D array with alternating0s and 1s, and -1s at the corners.  You will have to run itfor something like 10000 generations to get the perfect score.evolving...........................................................................................................................................................................................................the ga generated: -1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 -1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 -1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1  1 -1Example 18This program is designed to compare the GA types.  You canspecify steady-state, incremental, or simple GA and tweak anyof the parameters for each of these GA types.  The objectivefunction tries to match a pattern read in from a file.input pattern:                                          * * * * * *                 * *             * *           *                     *         *     * *     * *     *       *       * *     * *       *     *                         *     *                         *     *                         *       *   *             *   *         *     * * * * * *     *           * *             * *                 * * * * * *                                           the ga generated:      *                                   *   * * * *                   *             *             *     * *             *         *   * * *     * *     *   *   *       * *     * *       *     *         *               *     *                         *     *                     *   *       *   *                 *         *     * * * * * *     *           * *             * *       *         * * * * *                                             the statistics for the run are:400	# current generation0.990521	# current convergence12000	# number of selections since initialization10776	# number of crossovers since initialization2707	# number of mutations since initialization12000	# number of replacements since initialization11051	# number of genome evaluations since initialization201	# number of population evaluations since initialization211	# maximum score since initialization88	# minimum score since initialization182.65	# average of all scores ('on-line' performance)187.255	# average of maximum scores ('off-line' performance)178.162	# average of minimum scores ('off-line' performance)112.9	# mean score in initial population127	# maximum score in initial population88	# minimum score in initial population7.63996	# standard deviation of initial population-1	# diversity of initial population (0=identical,-1=unset)207.2	# mean score in current population211	# maximum score in current population203	# minimum score in current population2.49689	# standard deviation of current population-1	# diversity of current population (0=identical,-1=unset)20	# how far back to look for convergence10	# how often to record scores50	# how often to write scores to filebog.dat	# name of file to which scores are writtenthe objective function was called 11051 timesbest of generation data are in 'bog.dat'Example 19This program runs the DeJong test problems.running DeJong function number 1 ...the ga generated:5.11 -5.12 -5.12 the statistics for the run are:400	# current generation1	# current convergence3200	# number of selections since initialization2481	# number of crossovers since initialization152	# number of mutations since initialization2800	# number of replacements since initialization2524	# number of genome evaluations since initialization401	# number of population evaluations since initialization78.5409	# maximum score since initialization0.342654	# minimum score since initialization77.4228	# average of all scores ('on-line' performance)78.006	# average of maximum scores ('off-line' performance)77.0203	# average of minimum scores ('off-line' performance)25.943	# mean score in initial population76.0281	# maximum score in initial population0.342654	# minimum score in initial population14.6341	# standard deviation of initial population-1	# diversity of initial population (0=identical,-1=unset)78.5409	# mean score in current population78.5409	# maximum score in current population78.5409	# minimum score in current population0	# standard deviation of current population-1	# diversity of current population (0=identical,-1=unset)20	# how far back to look for convergence10	# how often to record scores50	# how often to write scores to filebog.dat	# name of file to which scores are writtenbest-of-generation data are in 'bog.dat'Example 20Running Holland's Royal Road test problem with a genome that is240 bits long (16 blocks).  The parameters are as follows: 	block size: 8	  gap size: 7	        m*: 4	        u*: 1	         u: 0.3	         v: 0.02the ga generated:101000110111000111111111100000111111111010001111111110010111011001100001010110001010011100111111110001001111111111110001100110011000000001011101011111110001101101000111111111001100000111100111001111111110101100110001100111101111111110011010the highest level achieved was 1the statistics for the run are:10000	# current generation1	# current convergence2560000	# number of selections since initialization2305756	# number of crossovers since initialization613723	# number of mutations since initialization2560000	# number of replacements since initialization2360486	# number of genome evaluations since initialization10001	# number of population evaluations since initialization5.04	# maximum score since initialization-0.06	# minimum score since initialization5.03473	# average of all scores ('on-line' performance)5.03726	# average of maximum scores ('off-line' performance)5.03398	# average of minimum scores ('off-line' performance)0.541289	# mean score in initial population1.9	# maximum score in initial population-0.06	# minimum score in initial population0.303989	# standard deviation of initial population-1	# diversity of initial population (0=identical,-1=unset)5.04	# mean score in current population5.04	# maximum score in current population5.04	# minimum score in current population0	# standard deviation of current population-1	# diversity of current population (0=identical,-1=unset)20	# how far back to look for convergence20	# how often to record scores100	# how often to write scores to filebog.dat	# name of file to which scores are writtenthe parameters for the run are:minimaxi	1number_of_generations	10000generations_to_convergence	20convergence_percentage	0.99crossover_probability	0.9mutation_probability	0.001population_size	512score_frequency	20flush_frequency	100record_diversity	0score_filename	bog.datselect_scores	2number_of_best	1replacement_percentage	0.5replacement_number	256Example 21This example shows various uses of the allele set objectin combination with the real number genome.running ga number 1 (alternate min/max values)...the ga generated:-10 10 -10 10 -10 10 -10 10 running ga number 2 (continuous descending order)...the ga generated:0.722113 0.690404 0.475627 0.443254 0.413628 0.307699 0.286382 0.0776234 running ga number 3 (discretized descending order)...the ga generated:7.5 5.5 4 1.5 0.5 7 4 0 running ga number 4 (maximize each gene)...the ga generated:10 100 -5 -0.0001 11000 Example 22This example shows how to derive your own genetic algorithmclass.  Here we use a custom, single-child crossover and amodified replacement strategy with overlapping populations.initializing...evolving..........dumping the function to file...initial population is in 'pop.initial.dat'final population is in 'pop.final.dat'the function is in 'sinusoid.dat'parameters were:minimaxi	1number_of_generations	500generations_to_convergence	20convergence_percentage	0.99crossover_probability	1mutation_probability	0.01population_size	100score_frequency	10flush_frequency	100record_diversity	0score_filename	bog.datselect_scores	255number_of_best	1replacement_percentage	0.25replacement_number	25Example 23This program tries to maximize or minimize, depending on thecommand line argument that you give it.  Use the command-lineargument 'mm -1' to minimize (the default for this example), or'mm 1' to maximize.  The objective function is a simple sinusoidal.printing initial population to file...printing final population to file...printing function to file...Example 24This example illustrates how to derive your own geneticalgorithm.  This genetic algorithm does restricted mating anduses a selector slightly more finicky than a uniform randomselector.  The objective function is a simple sinusoidal.printing population to file 'population.dat'...printing function to file 'sinusoid.dat'...Example 25This example uses a genetic algorithm with multiple populations.initializing...evolving.......................................................................................................best individual is: 11111111111111111111111111111111100	# current generation1	# current convergence13000	# number of selections since initialization12500	# number of crossovers since initialization12024	# number of mutations since initialization12500	# number of replacements since initialization12650	# number of genome evaluations since initialization1005	# number of population evaluations since initialization1	# maximum score since initialization0	# minimum score since initialization0.163021	# average of all scores ('on-line' performance)0.984688	# average of maximum scores ('off-line' performance)0	# average of minimum scores ('off-line' performance)0.1125	# mean score in initial population0.6875	# maximum score in initial population0	# minimum score in initial population0.255937	# standard deviation of initial population-1	# diversity of initial population (0=identical,-1=unset)0.166667	# mean score in current population1	# maximum score in current population0	# minimum score in current population0.379049	# standard deviation of current population-1	# diversity of current population (0=identical,-1=unset)20	# how far back to look for convergence1	# how often to record scores0	# how often to write scores to filegenerations.dat	# name of file to which scores are writtenExample 26The Travelling Salesman Problem (TSP) demo program.initializing...evolving...10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290 300 310 320 330 340 350 360 370 380 390 400 410 420 430 440 450 460 470 480 490 500 510 520 530 540 550 560 570 580 590 600 610 620 630 640 650 660 670 680 690 700 710 720 730 740 750 760 770 780 790 800 810 820 830 840 850 860 870 880 890 900 910 920 930 940 950 960 970 980 990 1000 the shortest path found is 22.6503this is the distance from the sequence15 11 10 6 7 3 2 1 0 20 4 5 9 8 12 16 17 13 14 18 19 1000	# current generation1	# current convergence100000	# number of selections since initialization100000	# number of crossovers since initialization10080	# number of mutations since initialization100000	# number of replacements since initialization100100	# number of genome evaluations since initialization1001	# number of population evaluations since initialization64.3227	# maximum score since initialization22.6503	# minimum score since initialization22.9415	# average of all scores ('on-line' performance)22.9859	# average of maximum scores ('off-line' performance)22.8274	# average of minimum scores ('off-line' performance)54.0745	# mean score in initial population64.3227	# maximum score in initial population44.4397	# minimum score in initial population4.07012	# standard deviation of initial population-1	# diversity of initial population (0=identical,-1=unset)22.6503	# mean score in current population22.6503	# maximum score in current population22.6503	# minimum score in current population1.18169e-06	# standard deviation of current population-1	# diversity of current population (0=identical,-1=unset)20	# how far back to look for convergence100	# how often to record scores0	# how often to write scores to filegenerations.dat	# name of file to which scores are writtenExample 27Deterministic crowding demonstration program.In addition to the standard GAlib command-line arguments,you can specify one of the four following functions:   0 - modified Himmelblau's function   1 - Foxholes (25)   2 - Schwefel's nasty (1 glob. Max bei (420.96/420.96)   3 - Mexican Hat (optimum at 0,0)best individual is 2.99843 2.00066 100	# current generation1	# current convergence10000	# number of selections since initialization5000	# number of crossovers since initialization245	# number of mutations since initialization975	# number of replacements since initialization5100	# number of genome evaluations since initialization101	# number of population evaluations since initialization10	# maximum score since initialization5.06767	# minimum score since initialization9.90361	# average of all scores ('on-line' performance)9.99994	# average of maximum scores ('off-line' performance)9.38956	# average of minimum scores ('off-line' performance)8.84823	# mean score in initial population9.99917	# maximum score in initial population5.06767	# minimum score in initial population1.05433	# standard deviation of initial population-1	# diversity of initial population (0=identical,-1=unset)9.99587	# mean score in current population10	# maximum score in current population9.95136	# minimum score in current population0.00846512	# standard deviation of current population-1	# diversity of current population (0=identical,-1=unset)20	# how far back to look for convergence100	# how often to record scores0	# how often to write scores to filegenerations.dat	# name of file to which scores are written