Question
Design and implement a genetic programming system to evolve some perceptrons that match well with a given training set. A training set is a collection
Design and implement a genetic programming system to evolve some perceptrons that match well with a given training set. A training set is a collection of tuples of the form (x1,...., xn, l), where xi's are real numbers and l is either 1 (positive example) or 0 (negative example). So for your genetic programming system, a "program" is just a tuple (w1, ...., wn ) of numbers (weights and the threshold). Answer the following questions: 1. What's your fitness function? 2. What's your crossover operator? 3. What's your copy operator? 4. What's your mutation operator, if you use any? 5. What's the size of the initial generation, and how are programs generated? 6. When do you stop the evolution? Evolve it up to a xed iteration, when it satisfies a condition on the fitness function, or a combination of the two? 7. What's the output of your system for the training set in the next page?
x1 x2 x3 x4 x5 x6 x7 x8 x9|Output 0.05 0.47 0.89 0.28 0.37 0.58 0.71 0.75 0.29 0.62 0.2 0.71 0.61 0.01 0.63 0.52 0.04 0.94 0.61 0.7 0.63 0.43 0.79 0.83 0.92 0.81 0.27 0.75 0.42 0.03 0.02 0.23 0.42 0.13 0.81 0.51 0.66 0.07 0.88 0.83 0.92 0.83 0.3 0.35 0.79 0.57 0.56 0.36 0.94 0.5 0.7 0.56 0.01 0.78 0.07 0.66 0.54 0.6 0.38 0.67 0.66 0.96 0.55 0.75 0.43 0.11 0.04 0.35 0.48 0.25 0.59 0.5 0.91 0.87 0.65 0.95 0.42 0.27 0.25 0.2 0.76 0.46 0.47 0.13 0.99 0.82 0.56 0.72 0.16 0.62 0.09 0.08 0.17 0.57 0.24 0.68 0.99 0.71 0.48 0.34 0.5 0.26 0.34 0.06 0.86 0.58 0.41 0.74 0.7 0.77 0.27 0.98 0.52 0.38 0.32 0.85 0.46 0.66 0.57 0.22 0.77 0.4 0.1 0.61 0.38 0.210 0.4 0.92 0.79 0.18 0.58 0.3 0.08 0.71 0.7 0.3 0.85 0.36 0.23 0.68 0.15 0.21 0.01 0.31 0.8 0.05 0.21 0.44 0.74 0.25 0.85 0.83 0.06 0.26 0.32 0.24 0.18 0.74 0.36 0.09 0.48 0.18 0.76 0.65 0.52 0.0 0.39 0.64 0.36 0.03 0.62 1 0.4 0.46 0.71 0.73 0.34 0.29 0.75 0.93 0.19 0.59 0.66 0.28 0.19 0.87 0.31 0.37 0.84 0.41 0.35 0.73 0.43 0.46 0.48 0.49 0.48 0.86 0.1 0.47 0.45 1.0 0.91 0.24 0.04 0.72 0.17 0.7 0.86 0.11 0.28 0.19 0.96 0.07 0.48 0.92 0.91 0.78 0.2 0.49 0.26 0.83 0.42 0.03 0.42 0.73 0.76 0.01 0.63 0.82 0.5 0.8 0.6 0.52 0.63 0.9 0.31 0.22 0.22 0.31 0.72 0.45 0.79 0.72 0.84 0.72 0.09 0.67 0.95 0.54 0.05 0.97 0.16 0.94 0.07 0.43 0.19 0.1 0.63 0.5 0.78 0.35 0 0.51 0.55 0.27 0.84 0.41 0.38 0.75 0.91 0.53 0 0.68 0.99 0.05 0.56 0.63 0.81 0.1 0.58 0.51 0.31 0.77 0.85 0.04 0.32 0.87 0.88 0.04 0.42 0.32 0.98 0.38 0.77 0.54 0.57 0.22 0.46 1.0 0.57 0.1 0.98 0.44 0.5 0.37 0.7 0.54 0.46 0.24 0.18 0.17 0.08 0.62 0.46 0.93 0.98 0.32 0.39 0.84 0.92 0.31 0.34 0.14 0.72 0.85 0.24 0.86 0.84 0.75 0.59 0.73 0.57 0.77 0.38 0.67 1 0.0 0.77 0.97 0.4 0.64 0.02 0.46 0.84 0.14 0.74 0.7 0.6 0.79 0.34 0.24 0.64 0.82 0.38 0 0.71 0.36 0.95 0.23 0.08 0.46 0.96 0.04 0.28 0.61 0.03 0.67 0.83 0.03 0.46 0.58 0.36 0.21 0.72 0.87 0.45 0.48 0.47 0.61 0.67 0.28 0.45 0.23 0.66 0.03 0.04 0.44 0.23 0.76 0.11 0.77 0.35 0.21 0.69 0.32 0.41 0.91 0.44 0.13 0.49 0.92 0.25 0.34 0.85 0.12 0.37 0.23 0.79 0.81 0.58 0.68 0.26 0.83 0.97 0.78 0.3 0.14 0.9 0.24 0.85 0.91 0.24 0.98 0.97 0.05 0.03 0.08 0.35 0.72 0.87 0.66 0.58 0.79 0.19 0.86 0.85 0.38 0.93 0.96 0.05 0.4 0.33 0.06 0.09 0.27 0 0 x1 x2 x3 x4 x5 x6 x7 x8 x9|Output 0.05 0.47 0.89 0.28 0.37 0.58 0.71 0.75 0.29 0.62 0.2 0.71 0.61 0.01 0.63 0.52 0.04 0.94 0.61 0.7 0.63 0.43 0.79 0.83 0.92 0.81 0.27 0.75 0.42 0.03 0.02 0.23 0.42 0.13 0.81 0.51 0.66 0.07 0.88 0.83 0.92 0.83 0.3 0.35 0.79 0.57 0.56 0.36 0.94 0.5 0.7 0.56 0.01 0.78 0.07 0.66 0.54 0.6 0.38 0.67 0.66 0.96 0.55 0.75 0.43 0.11 0.04 0.35 0.48 0.25 0.59 0.5 0.91 0.87 0.65 0.95 0.42 0.27 0.25 0.2 0.76 0.46 0.47 0.13 0.99 0.82 0.56 0.72 0.16 0.62 0.09 0.08 0.17 0.57 0.24 0.68 0.99 0.71 0.48 0.34 0.5 0.26 0.34 0.06 0.86 0.58 0.41 0.74 0.7 0.77 0.27 0.98 0.52 0.38 0.32 0.85 0.46 0.66 0.57 0.22 0.77 0.4 0.1 0.61 0.38 0.210 0.4 0.92 0.79 0.18 0.58 0.3 0.08 0.71 0.7 0.3 0.85 0.36 0.23 0.68 0.15 0.21 0.01 0.31 0.8 0.05 0.21 0.44 0.74 0.25 0.85 0.83 0.06 0.26 0.32 0.24 0.18 0.74 0.36 0.09 0.48 0.18 0.76 0.65 0.52 0.0 0.39 0.64 0.36 0.03 0.62 1 0.4 0.46 0.71 0.73 0.34 0.29 0.75 0.93 0.19 0.59 0.66 0.28 0.19 0.87 0.31 0.37 0.84 0.41 0.35 0.73 0.43 0.46 0.48 0.49 0.48 0.86 0.1 0.47 0.45 1.0 0.91 0.24 0.04 0.72 0.17 0.7 0.86 0.11 0.28 0.19 0.96 0.07 0.48 0.92 0.91 0.78 0.2 0.49 0.26 0.83 0.42 0.03 0.42 0.73 0.76 0.01 0.63 0.82 0.5 0.8 0.6 0.52 0.63 0.9 0.31 0.22 0.22 0.31 0.72 0.45 0.79 0.72 0.84 0.72 0.09 0.67 0.95 0.54 0.05 0.97 0.16 0.94 0.07 0.43 0.19 0.1 0.63 0.5 0.78 0.35 0 0.51 0.55 0.27 0.84 0.41 0.38 0.75 0.91 0.53 0 0.68 0.99 0.05 0.56 0.63 0.81 0.1 0.58 0.51 0.31 0.77 0.85 0.04 0.32 0.87 0.88 0.04 0.42 0.32 0.98 0.38 0.77 0.54 0.57 0.22 0.46 1.0 0.57 0.1 0.98 0.44 0.5 0.37 0.7 0.54 0.46 0.24 0.18 0.17 0.08 0.62 0.46 0.93 0.98 0.32 0.39 0.84 0.92 0.31 0.34 0.14 0.72 0.85 0.24 0.86 0.84 0.75 0.59 0.73 0.57 0.77 0.38 0.67 1 0.0 0.77 0.97 0.4 0.64 0.02 0.46 0.84 0.14 0.74 0.7 0.6 0.79 0.34 0.24 0.64 0.82 0.38 0 0.71 0.36 0.95 0.23 0.08 0.46 0.96 0.04 0.28 0.61 0.03 0.67 0.83 0.03 0.46 0.58 0.36 0.21 0.72 0.87 0.45 0.48 0.47 0.61 0.67 0.28 0.45 0.23 0.66 0.03 0.04 0.44 0.23 0.76 0.11 0.77 0.35 0.21 0.69 0.32 0.41 0.91 0.44 0.13 0.49 0.92 0.25 0.34 0.85 0.12 0.37 0.23 0.79 0.81 0.58 0.68 0.26 0.83 0.97 0.78 0.3 0.14 0.9 0.24 0.85 0.91 0.24 0.98 0.97 0.05 0.03 0.08 0.35 0.72 0.87 0.66 0.58 0.79 0.19 0.86 0.85 0.38 0.93 0.96 0.05 0.4 0.33 0.06 0.09 0.27 0 0Step by Step Solution
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