1. Evolving Ecosystem Models Using
Genetic Programming
ECOSYSTEM MODEL CONVERSION
Current ecosystem models are built that prefer:
• Deterministic over chaotic behavior
• Canonical approaches to ecosystem dynamics
And assume:
• The number and type of equations used to
describe interactions
• The level of detail necessary to model a given
ecosystem
Based on these assumptions current modeling
techniques produce models that may be overly-
simplified or complex, and may be ill-suited for
simulating and ultimately predicting the range of
global environments due to varying local ecosystem
conditions that are unresolved within the models.
• How do we parameterize unknown unknowns?
• How can we account for an evolving world with
static models?
We propose to use genetic programming techniques
to create populations of models along with
populations of parameter sets for each model, that
can be analyzed for best fit to observational data and
may reveal new dynamics, identify extraneous terms
in current models, and provide a framework for model
generation that evolves as the environment changes.
The genetic algorithm will:
• Randomly generate populations of ecosystem
models using current ecosystem models as seed
stock
• Randomly choose parameters sets for these model
populations and rate the fitness of these
model/parameter families based on their ability to
reproduce observed data
• Asexually reproduce the fittest models
• Sexually reproduce random models of varying
fitness
• Occasionally introduce random mutations in model
equations
Once a family of models and parameter sets agree
with observed data to within a specified tolerance,
models can be studied to identify any new or different
generated equations in the hopes that they provide
novel insight into ecosystem dynamics.
To begin a genetic approach to ecosystem modeling, we must implement a
protocol that allows us to convert ecosystems models from mathematical
equations to a form that can be easily manipulated by our algorithm. We:
• Cast a given ecosystem model into a source/sink matrix in order to succinctly
represent the bio-flow of species and resources
• Validate each ecosystem conversion by running the model
and reproducing similar results as those reported in the
literature
• Express each equation in a matrix cell as a binary tree, that will our allow our
algorithm to treat equations as “genes” available for recombination
Graphs produced via Direct
Computation
Graphs produced via Tree
Computation
ABSTRACT VALIDATION
Fasham et. al.
Huisman and Weissing
Our next steps include converting ecosystem models
from as many different modeling paradigms as possible
to our proposed data structure in order to validate that
our approach is viable for the wide range of ecosystem
models that currently exist. Once enough models have
been converted, they will be used as seed stock and the
actual work evolving new models can begin.
NEXT STEPS
ACKNOWLEDGEMENTS
REFERENCES
Support for this work was provided by NASA Goddard
Space Flight Center, Oregon NASA Space Grant
Consortium (OSGC), and NASA USRP.
Fasham et al.: Mixed-layer plankton dynamics model,
Journal of Marine Research, 48, 591-639, 1990.
Jef Huisman & Franz J. Weissing: Biodiversity of
plankton by species oscillations and chaos, Nature
Vol. 402, 25 Nov 1999
David Coulter Portland Community College
Erik Wisuri Northern Michigan University
Dr. John Moisan NASA GSFC