# What qualifies as 'fitness' for a genetic algorithm that minimizes an error function?

Suppose I have a set of data that I want to apply a segmented regression to, fitting linearly across the breakpoint. I aim to find the offsets and slopes of either line and the position of the breakpoint that minimizes an error function given the data I have, and then use them as sufficiently close initial guesses to find the exact solutions using a curve fit. I'll elect to choose the bounds as the mins and maxes of $$x$$ and $$y$$ of my data, and an arbitrary bound for a slope with $$slope = a * \frac{y_{max}-y_{min}}{x_{max}-x_{min}}$$ for a suitable $$a$$ where I can safely assume the magnitude of $$a$$ is greater than any possible slope that realistically represents the data. Let's suppose I define a function (in Python):

 def generate_genetic_Parameters():
initial_parameters=[]
x_max=np.max(xData)
x_min=np.min(xData)
y_max=np.max(yData)
y_min=np.min(yData)
slope=10*(y_max-y_min)/(x_max-x_min)

initial_parameters.append([x_max,x_min]) #Bounds for module break point
initial_parameters.append([-slope,slope]) #Bounds for slopeA
initial_parameters.append([-slope,slope]) #Bounds for slopeB
initial_parameters.append([y_max,y_min]) #Bounds for offset A
initial_parameters.append([y_max,y_min]) #Bounds for offset B

result=differential_evolution(sumSquaredError,initial_parameters,seed=3)

return result.x

geneticParameters = generate_genetic_Parameters() #Generates genetic parameters

fittedParameters, pcov= curve_fit(func, xData, yData, geneticParameters)


This will do the trick, but what is the implicit standard of fitness that the differential evolution here deals with?