This was part of my master's thesis. It extends Neurofitter to use multiple objectives (error functions) with an evolutionary algorithm (MOEA) to search for the best fit of neuronal model parameters. It compares model output to experimental data, and tries to find the best fit for model parameters by comparing spiking rates, action potential height and width, afterhyperpolarization depth and latency to first spike. The parameters are updated using an evolutionary strategy. DISCLAIMER: I did not build neurofitter, i just added components for the MOEA optimiation and search.