# Calibration by monte carlo, should I fix my seed?

I am calibrating a 3-parameter stochastic model to options market data via Monte Carlo simulation. Let the parameter set be denoted by $\bar{\theta}$. (this is not a simple Black-Scholes type model, so MC calibration is the only possible way of calibrating this model)

Now, my question is whether I should have a fixed seed for my objective function evaluation, with the objective function being the mean squared error between the simulated options value and the market implied options value. Meaning that every time my optimizer makes a call to the objective function with a perturbed parameter set $\theta+\delta$, I am using the same random numbers. Effectively eliminating Monte Carlo noise. But is this 'cheating', if so the use of finite difference gradients for this type of problems are useless (or?)