Evolutionary Multi-Objective Optimization (EMOO)
Evolutionary optimization is an established tool to
explore complex parameter spaces using strategies from biological
evolution to select, modify and breed new models. Classically the
quality of a model is determined based on a single distance
function. But if multiple properties of a model are to be optimized
a rather arbitrary weighting of these properties is required to
create a single distance function again.
Multi-objective optimization strategies instead optimize multiple
and possibly confliciting distance functions at the same time which
allows to target noisy data. This powerful optimization method is
therefore well suited for optimizing multi-compartmental neurons
models to experimental data and has already been successfully used
in recent modelling studies in neuroscience (Druckmann et al. 2007;
Druckmann et al. 2008; Bahl 2009; Hay et al. 2011; Bahl et al.
Here I provide this optimization framework which I have used to
optimize a 17 parameter model of a layer 5 pyramidal neuron to
experimental data. It is entirely written in Python and uses the
Message Passing Interface (MPI) for communication between different
processors and nodes.
Please contact me if you have comments, improvements or questions.
Enjoy your optimization!
- I strongly recommend to read the book
by Kalyanmoy Deb
- To get the full power of running MPI on a cluster, you should
install a job scheduling engine like Torque.
- Download and extract emoo_1_0.zip
- Copy the folder emoo to your Python-library path (e.g.
site-packages), or add the path to the folder containing emoo to
your python path.
- This should be all; emoo can be imported as a Python-module
- I have tested emoo on my MacBook Pro (OSX Lion) and under
Ubuntu Linux, but it should also work under windows.
- emoo can be imported as a standard python module, it should
check itself whether mpi4py is properly installed.
- running your script on a single core without using mpi:
- running directly on multiple processors using MPI:
- mpirun -np 2
- put in the number of processors (-np x) you would like to
- using the job scheduling engine
- Example 1: Minimize a
single distance function with three parameters
- Example 2: Minimize two
conflicting distance functions with two parameters
- Example 3: An application
to neuroscience: Find ion channel densities to tune a neuron
- bahl "an at here" neuro "a dot here" mpg "another dot here" de
Deb, K. (2001). Multi-objective optimization using evolutionary
algorithms. New York, NY, USA: John Wiley & Sons, Inc.
Druckmann, S., Banitt, Y., Gidon, A., Schürmann, F., Markram,
H., & Segev, I. (2007). A novel multiple objective optimization
framework for constraining conductance-based neuron models by
experimental data. Frontiers in Neuroscience, 1(1), 7–18.
Druckmann, S., Berger, T. K., Hill, S., Schürmann, F., Markram,
H., & Segev, I. (2008). Evaluating automated parameter
constraining procedures of neuron models by experimental and
surrogate data. Biological Cybernetics, 99(4-5), 371–379.
Bahl A (2009). Automated optimization of a reduced layer 5 pyramidal
cell model based on experimental data. Diplomarbeit (HU-Berlin)
Bahl A, Stemmler MB, Herz AVM, Roth A. (2012). Automated
optimization of a reduced layer 5 pyramidal cell model based on
experimental data. J Neurosci Methods.
Automated optimization of a reduced layer 5 pyramidal cell
model based on experimental data