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The Genetic Algorithm is a Stochastic Hill climbing algorithm based upon the metaphor of population

genetics. It is a weak method that utilizes an objective function in order to guide performance. Here the objective function is the ONE MAX problem. This is a very simple example of what is called a symbolic regression problem. The goal is when given a random string of 0's and 1's, to optimize the count of the bits in the string. E.G. Produce a string of all ones. Brownlee, pp: 92-98.

Download the Ruby GA program that optimizes the performance function above. Plot the learning curve for the program. That is, for each generation plot the best value evolved so far. Describe how learning takes place here in terms of the graph. Does the learning process converge to the optimal function value? Describe

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