Genetic Algorithm with Java

Implement the genetic algorithm (GA): Maximize f(X1, X2, X3) = (X1)^2 + (X2)^2 + (X3)^2, for -1.0 <= Xi <= 1.0, i = 1, 2, 3. That is, the goal is to find values of X1, X2, and X3 (in the given interval) that will cause f(.,.,.) to have the maximum value. Use real (floating-point) values, not bit-string representations. Use proportional selection, single-point crossover, and gene-wise mutation. Use the following parameter values: N = 20; pc = 0.6 to 1.0; pm = 0.05 to 0.3; max generations (for use as a stopping condition) = 50. For each of three “typical” single runs, report the following: At intervals of 10 generations, the best and the worst fitnesses in the current generation and the corresponding X-vectors; the average fitness of all the members of the current generation; the best-of-run solution (the vector and its fitness). Next, collect data on 30 independent runs, and then report, at intervals of 10 generations, the following averages (and the corresponding standard deviations) over 30 items: the best-of-generation fitness, the average-of-generation fitness, the best-of-run fitness. That is, these three quantities are each an average of 30 values.