protected override List <List <double> > GenerateValues()
        {
            var rand = new MersenneTwister((uint)Seed);

            List <List <double> > data = new List <List <double> >();
            var x1 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.4, 0.8).ToList();

            var x2 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 3.0, 4.0).ToList();

            var x3 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 20.0, 30.0).ToList();

            var x4  = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 2.0, 5.0).ToList();
            var x13 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 2.0, 5.0).ToList();
            var x16 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 2.0, 5.0).ToList();


            // in the reference paper \Delta alpha_w/c is replaced by two variables x5*x6.
            var x5 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0, 20).ToList(); // range for X5 is not specified in the paper, we use [0°..20°] for ∆αW/c
            var x6 = Enumerable.Repeat(1.0, x5.Count).ToList();                                                      // range for X6 is not specified in the paper. In the maximum lift formular there is only a single variable ∆αW/c in place of x5*x6.

            var x7  = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.5, 1.5).ToList();
            var x10 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 0.5, 1.5).ToList();

            var x8  = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 1.5).ToList();
            var x11 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 1.5).ToList();

            var x9  = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 2.0).ToList();
            var x12 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 2.0).ToList();

            var x14 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 1.5).ToList();
            var x17 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 1.0, 1.5).ToList();

            var x15 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 5.0, 7.0).ToList();

            var x18 = ValueGenerator.GenerateUniformDistributedValues(rand.Next(), TestPartitionEnd, 10.0, 20.0).ToList();


            List <double> fx       = new List <double>();
            List <double> fx_noise = new List <double>();

            data.Add(x1);
            data.Add(x2);
            data.Add(x3);
            data.Add(x4);
            data.Add(x5);
            data.Add(x6);
            data.Add(x7);
            data.Add(x8);
            data.Add(x9);
            data.Add(x10);
            data.Add(x11);
            data.Add(x12);
            data.Add(x13);
            data.Add(x14);
            data.Add(x15);
            data.Add(x16);
            data.Add(x17);
            data.Add(x18);
            data.Add(fx);
            data.Add(fx_noise);

            for (int i = 0; i < x1.Count; i++)
            {
                double fxi = x1[i];
                fxi = fxi - 0.25 * x4[i] * x5[i] * x6[i] * (4 + 0.1 * (x2[i] / x3[i]) - (x2[i] / x3[i]) * (x2[i] / x3[i]));
                fxi = fxi + x13[i] * (x14[i] / x15[i]) * x18[i] * x7[i];
                fxi = fxi - x13[i] * (x14[i] / x15[i]) * x8[i];
                fxi = fxi + x13[i] * (x14[i] / x15[i]) * x9[i];
                fxi = fxi + x16[i] * (x17[i] / x15[i]) * x18[i] * x10[i];
                fxi = fxi - x16[i] * (x17[i] / x15[i]) * x11[i];
                fxi = fxi + x16[i] * (x17[i] / x15[i]) * x12[i];

                fx.Add(fxi);
            }

            var sigma_noise = 0.05 * fx.StandardDeviationPop();

            fx_noise.AddRange(fx.Select(fxi => fxi + NormalDistributedRandom.NextDouble(rand, 0, sigma_noise)));

            return(data);
        }