#%% helper function to print arrays to log Progeny = np.append(progeny,, axis=0)Īrray * mate2 + array * (1 - mate2) + mutation2 * mate2 # selecting 6 individuals from the intervals #%% define function to carry out stochastic universal sampling # setting ranks based on position in the pool
# normalization factor for selection pressure 3 and individuals 50 #%% function to give non linear rank to sorted pool lower is betterįor x, y in zip(initial, initial): # score is the total square deviation from initial. # take care to always keep one column for fitness values in starting array # takes an array as input and fills the last column with fitness values
#%% define a function to return the polynomial Genetic Algorithm implementation of finding coefficients of a polynomialģ.0 - 4.3 * x + 5.9 * x ** 2 - 5.2 * x ** 3 + 1.0 * x ** 4 = 0 Please suggest improvements where possible. The worst individuals in the starting pool are then replaced by progeny. Individuals are then selected and real valued crossover is used to generate progeny, who are also sorted according to LSD. These ranks are then converted into nonlinear ranking and mapped to a line of unit length. These polynomials are then ranked based on the least square difference (LSD) and sorted such that lower LSD has higher rank. I then generate 100 polynomials with randomly selected coefficients. The information initially provided is values of y = f(x) for different x using the original polynomial.
#IGOR PRO GUESS COEFFICIENT CODE#
I have tried to code a genetic algorithm to guess the coefficients of a degree 4 polynomial.
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