/// <summary>
        ///     The entry point for this example.  If you would like to make this example
        ///     stand alone, then add to its own project and rename to Main.
        /// </summary>
        /// <param name="args">Not used.</param>
        public static void ExampleMain(string[] args)
        {
            // Create a new population.
            IPopulation pop = new BasicPopulation();
            ISpecies species = pop.CreateSpecies();

            // Create 1000 genomes, assign the score to be the index number.
            for (int i = 0; i < 1000; i++)
            {
                IGenome genome = new IntegerArrayGenome(1);
                genome.Score = i;
                genome.AdjustedScore = i;
                pop.Species[0].Add(genome);
            }

            IGenerateRandom rnd = new MersenneTwisterGenerateRandom();

            // Create a trainer with a very simple score function.  We do not care
            // about the calculation of the score, as they will never be calculated.
            // We only care that we are maximizing.
            IEvolutionaryAlgorithm train = new BasicEA(pop, new NullScore());

            // Perform the test for round counts between 1 and 10.
            for (int roundCount = 1; roundCount <= 10; roundCount++)
            {
                var selection = new TournamentSelection(train, roundCount);
                int sum = 0;
                int count = 0;
                for (int i = 0; i < 100000; i++)
                {
                    int genomeID = selection.PerformSelection(rnd, species);
                    IGenome genome = species.Members[genomeID];
                    sum += (int) genome.AdjustedScore;
                    count++;
                }
                sum /= count;
                Console.WriteLine("Rounds: " + roundCount + ", Avg Score: " + sum);
            }
        }
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        /// <summary>
        ///     Construct an EA.
        /// </summary>
        /// <param name="thePopulation">The population.</param>
        /// <param name="theScoreFunction">The score function.</param>
        public BasicEA(IPopulation thePopulation,
            IScoreFunction theScoreFunction)
        {
            RandomNumberFactory = new MersenneTwisterFactory();
            EliteRate = 0.3;
            MaxTries = 5;
            MaxOperationErrors = 500;
            CODEC = new GenomeAsPhenomeCODEC();


            Population = thePopulation;
            ScoreFunction = theScoreFunction;
            Selection = new TournamentSelection(this, 4);

            // set the score compare method
            if (theScoreFunction.ShouldMinimize)
            {
                SelectionComparer = new MinimizeAdjustedScoreComp();
                BestComparer = new MinimizeScoreComp();
            }
            else
            {
                SelectionComparer = new MaximizeAdjustedScoreComp();
                BestComparer = new MaximizeScoreComp();
            }

            // set the iteration
            foreach (ISpecies species in thePopulation.Species)
            {
                foreach (IGenome genome in species.Members)
                {
                    IterationNumber = Math.Max(IterationNumber,
                        genome.BirthGeneration);
                }
            }


            // Set a best genome, just so it is not null.
            // We won't know the true best genome until the first iteration.
            if (Population.Species.Count > 0 && Population.Species[0].Members.Count > 0)
            {
                BestGenome = Population.Species[0].Members[0];
            }
        }